{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0c365d3a",
   "metadata": {},
   "source": [
    "# U.S. crude oil supply, demand, and stocks\n",
    "\n",
    "Estimate monthly inventory builds and draws, forecast commercial stocks, and compare forecast accuracy with seasonal and unchanged-stock benchmarks.\n",
    "\n",
    "Note: Models used in this notebook are for fun. I wanted to use the models that I have learned in my courses in school. The models aren't meant to be accurate as there is lots of information I do not have access to make an accurate model. For example, I do not have access to refinery turnarounds, shipping data, etc. This notebook is meant to help me gain a better understanding of EIA data as well as how basic supply and demand is supposed to be modeled."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "data-refresh-20260916",
   "metadata": {},
   "source": [
    "### Data refresh — September 16, 2026\n",
    "\n",
    "Downloaded EIA data again. The latest month is still June 2026, and the model inputs are unchanged. The full rerun gives the same model choices, forecasts, and error figures. `seasonal_change` remains the one-month method and `constrained_level` remains the twelve-month method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ac1f4af",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "Use monthly EIA data from January 2007 for the five Petroleum Administration for Defense Districts (PADDs). Commercial crude oil stocks exclude the Strategic Petroleum Reserve (SPR)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85718a64",
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    "execution": {
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   "source": [
    "# Run cells from top to bottom: later cells reuse the data, functions, and fitted\n",
    "# models created here. pandas/NumPy handle monthly tables and arrays, sklearn\n",
    "# and XGBoost fit models, matplotlib draws charts, and joblib saves estimators.\n",
    "from pathlib import Path\n",
    "import json, platform, warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import joblib, sklearn, xgboost\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "from sklearn.base import BaseEstimator, RegressorMixin\n",
    "from sklearn.linear_model import LinearRegression, Ridge, HuberRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, VotingRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures, SplineTransformer\n",
    "from sklearn.compose import TransformedTargetRegressor\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.model_selection import GridSearchCV, TimeSeriesSplit\n",
    "from xgboost import XGBRegressor\n",
    "from IPython.display import display\n",
    "\n",
    "# Only input data are external. No local Python module is imported.\n",
    "# Support execution from either the product folder or the commodities workspace.\n",
    "# Fail early if the saved EIA observation CSV cannot be found.\n",
    "HERE=Path.cwd()\n",
    "if not (HERE/'data/eia_observations.csv').exists():\n",
    "    HERE=HERE/'oil'/'us_snd_crude'\n",
    "assert (HERE/'data/eia_observations.csv').exists(), 'Keep the data folder beside this notebook.'\n",
    "OUTPUT=HERE/'model_output'\n",
    "# These settings affect chart/table presentation only, not stored model values.\n",
    "plt.style.use('seaborn-v0_8-whitegrid')\n",
    "pd.set_option('display.max_columns',20)\n",
    "pd.set_option('display.float_format',lambda x:f'{x:,.2f}')\n",
    "\n",
    "# Dates represent observation months using their first day. Both exclusion\n",
    "# endpoints are inclusive; keep the raw calendar intact so lags retain their meaning.\n",
    "COVID_START = pd.Timestamp('2020-03-01')\n",
    "COVID_END = pd.Timestamp('2021-03-01')\n",
    "\n",
    "\n",
    "# Return one Boolean per date: True means outside the excluded COVID interval.\n",
    "def outside_covid(months):\n",
    "    dates = pd.DatetimeIndex(months)\n",
    "    return ~((dates >= COVID_START) & (dates <= COVID_END))\n",
    "\n",
    "\n",
    "# Filter fitting rows after constructing lagged features on the intact calendar.\n",
    "# A clean target is insufficient if one of its direct lag inputs is excluded.\n",
    "def training_rows(frame):\n",
    "    \"\"\"Exclude COVID targets and any training row whose direct inputs touch COVID.\"\"\"\n",
    "    dates = pd.DatetimeIndex(frame.month)\n",
    "    keep = outside_covid(dates)\n",
    "    # lag1 stocks/flows, lag2 for the latest change, lag12 for annual stocks.\n",
    "    for lag in [1, 2, 12]:\n",
    "        keep &= outside_covid(dates - pd.DateOffset(months=lag))\n",
    "    return frame.loc[keep]\n",
    "\n",
    "\n",
    "# Create chronological expanding-window folds; never shuffle time-series rows.\n",
    "# This filter concerns target dates; fitting applies the stricter lag-input filter.\n",
    "def validation_splits(frame, folds=10, test_size=None):\n",
    "    \"\"\"Split eligible target dates, retaining real calendar positions and lag dates.\"\"\"\n",
    "    allowed = np.flatnonzero(outside_covid(frame.month))\n",
    "    splitter = TimeSeriesSplit(n_splits=folds, test_size=test_size)\n",
    "    # Map positions within eligible dates back to the original frame. A test block\n",
    "    # of 12 eligible observations may span more than 12 calendar months.\n",
    "    return [(allowed[tr], allowed[te]) for tr, te in splitter.split(allowed)]\n",
    "\n",
    "\n",
    "# Search backward in annual steps for six eligible year-long development paths.\n",
    "# Each origin leaves its next 12 forecast months inside development history.\n",
    "def recursive_origins(development_end, count=6):\n",
    "    \"\"\"Use six full test years that do not overlap the excluded interval.\"\"\"\n",
    "    origins = []\n",
    "    cutoff = development_end - pd.DateOffset(years=1)\n",
    "    while len(origins) < count:\n",
    "        months = pd.date_range(cutoff + pd.offsets.MonthBegin(), periods=12, freq='MS')\n",
    "        if outside_covid(months).all() and outside_covid([cutoff]).all():\n",
    "            origins.append(cutoff)\n",
    "        cutoff -= pd.DateOffset(years=1)\n",
    "    return sorted(origins)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe366d6",
   "metadata": {},
   "source": [
    "### Sources and units\n",
    "\n",
    "Inputs include production, imports, exports, net receipts, adjustments, transfers, refinery inputs, direct crude use, capacity, and stocks. Stocks are in thousand barrels and flows are in thousand barrels per day. Monthly flow totals are divided by days in the given month.\n",
    "\n",
    "The series map below lists the EIA identifiers and balance signs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "82f3e558",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# PADDs are the five Petroleum Administration for Defense Districts. Series\n",
    "# templates substitute {p} with the district number when identifying source data.\n",
    "PADD_NAMES = {1: 'East Coast', 2: 'Midwest', 3: 'Gulf Coast', 4: 'Rocky Mountain', 5: 'West Coast'}\n",
    "CODES = {'production_kbd': 'MCRFPP{p}1', 'demand_kbd': 'MCRRIP{p}1',\n",
    "         'imports_kbd': 'MCRIMP{p}1', 'exports_kbd': 'MCREXP{p}1',\n",
    "         'net_receipts_kbd': 'MCRNRP{p}1', 'adjustments_kbd': 'MCRUA_R{p}0_1',\n",
    "         'transfers_kbd': 'M_EPC0_TVP_R{p}0_MBBL', 'direct_use_kbd': 'MCRUPP{p}1',\n",
    "         'stock_kb': 'MCESTP{p}1', 'cdu_capacity_kbd': 'MOCLEP{p}2'}\n",
    "# Keep this order aligned with SIGNS: the dot product uses +1 for supply\n",
    "# additions and -1 for disposition to reconstruct net supply.\n",
    "FLOWS = ['production_kbd', 'demand_kbd', 'imports_kbd', 'exports_kbd',\n",
    "         'net_receipts_kbd', 'adjustments_kbd', 'transfers_kbd', 'direct_use_kbd']\n",
    "SIGNS = [1, -1, 1, -1, 1, 1, 1, -1]\n",
    "# The downloaded transfer series start in January 2022; earlier rows are structural zeros.\n",
    "SPARSE = {'exports_kbd', 'transfers_kbd', 'net_receipts_kbd'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b804c6f",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-09-16T22:03:47.060009Z"
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   },
   "outputs": [],
   "source": [
    "# Return one row per PADD/month with flows, month-end stocks, assumption flags,\n",
    "# and balance diagnostics, starting from the saved long-form EIA observations.\n",
    "def load_panel(data_dir=HERE / 'data', start='2007-01-01'):\n",
    "    # Parse timestamps for exact calendar reindexing and month-length calculations.\n",
    "    raw = pd.read_csv(Path(data_dir) / 'eia_observations.csv', parse_dates=['month'])\n",
    "    # Raw flow snapshots retain EIA monthly volumes; convert exactly once here.\n",
    "    raw['component'] = raw.component.replace({c.replace('_kbd', '_kb'): c for c in FLOWS})\n",
    "    raw = raw[raw.component.isin(FLOWS + ['commercial_stock_kb', 'cdu_capacity_kbd'])].copy()\n",
    "    raw['component'] = raw.component.replace({'commercial_stock_kb': 'stock_kb'})\n",
    "    raw['value'] = raw.value.astype(float)\n",
    "    is_flow = raw.component.isin(FLOWS)\n",
    "    raw.loc[is_flow, 'value'] /= raw.loc[is_flow, 'month'].dt.days_in_month\n",
    "    # The pivot requires a single observation per month/PADD/component combination.\n",
    "    if raw.duplicated(['month', 'padd', 'component']).any():\n",
    "        raise ValueError('Duplicate EIA observations')\n",
    "    panels = []\n",
    "    for p in PADD_NAMES:\n",
    "        r = raw[raw.padd.eq(p)]\n",
    "        # Use the latest observed stock month as this district's provisional endpoint.\n",
    "        end = r[r.component.eq('stock_kb') & r.value.notna()].month.max()\n",
    "        index = pd.date_range(start, end, freq='MS')\n",
    "        # Turn component names into columns, then expose absent months on a continuous\n",
    "        # month-start calendar. Reindexing does not interpolate missing observations.\n",
    "        wide = r.pivot(index='month', columns='component', values='value').reindex(index)\n",
    "        for c in SPARSE:\n",
    "            rows = r[r.component.eq(c)].set_index('month')\n",
    "            # Missing calendar rows in sparse export/transfer sheets are an explicit\n",
    "            # no-reported-flow assumption, not interpolation from future values.\n",
    "            absent = ~wide.index.isin(rows.index)\n",
    "            # Combine source zero flags with wholly absent sparse-series rows. An explicit\n",
    "            # unavailable value on an existing row remains missing instead of becoming zero.\n",
    "            wide[c + '_assumed_zero'] = rows.assumed_zero.reindex(index).astype(\"boolean\").fillna(False).astype(bool) | absent\n",
    "            wide.loc[absent, c] = 0.0\n",
    "        wide['padd'] = p\n",
    "        wide['padd_name'] = PADD_NAMES[p]\n",
    "        wide.index.name = 'month'\n",
    "        panels.append(wide.reset_index())\n",
    "    panel = pd.concat(panels, ignore_index=True)\n",
    "    core = FLOWS + ['stock_kb', 'cdu_capacity_kbd']\n",
    "    # Find the last month complete in all five districts, then trim to a shared\n",
    "    # endpoint so national totals compare the same observation months.\n",
    "    valid = panel[core].notna().all(axis=1)\n",
    "    common = panel[valid].groupby('month').padd.nunique()\n",
    "    end = common[common.eq(5)].index.max()\n",
    "    panel = panel[panel.month.le(end)].copy()\n",
    "    # Earlier unresolved gaps still fail validation: trimming the endpoint does not\n",
    "    # authorize filling withheld values or dropping an incomplete district.\n",
    "    if panel[core].isna().any().any():\n",
    "        raise ValueError('Unresolved missing core data; inspect the source snapshots')\n",
    "    if not panel.groupby('padd').size().eq(len(pd.date_range(start, end, freq='MS'))).all():\n",
    "        raise ValueError('Incomplete PADD calendar')\n",
    "    if panel.cdu_capacity_kbd.le(0).any():\n",
    "        raise ValueError('CDU capacity must be positive')\n",
    "    panel['utilization_ratio'] = panel.demand_kbd / panel.cdu_capacity_kbd\n",
    "    # Shift within each district so prior stock never comes from another PADD.\n",
    "    # The first month has no predecessor and therefore a missing stock change.\n",
    "    panel['previous_stock_kb'] = panel.groupby('padd').stock_kb.shift(1)\n",
    "    # Sum signed daily flows. Compare this with stock change divided by month length\n",
    "    # so both sides of the crude reconciliation are in thousand barrels per day.\n",
    "    panel['balance_kbd'] = panel[FLOWS].to_numpy() @ SIGNS\n",
    "    panel['stock_change_kb'] = panel.stock_kb - panel.previous_stock_kb\n",
    "    # Despite its legacy name, this diagnostic uses four flows: production plus\n",
    "    # imports minus refinery demand and exports. The remaining terms are omitted.\n",
    "    panel['five_term_balance_kbd'] = panel.production_kbd + panel.imports_kbd - panel.demand_kbd - panel.exports_kbd\n",
    "    panel['five_term_residual_kbd'] = panel.stock_change_kb / panel.month.dt.days_in_month - panel.five_term_balance_kbd\n",
    "    panel['accounting_residual_kbd'] = panel.stock_change_kb / panel.month.dt.days_in_month - panel.balance_kbd\n",
    "    return panel"
   ]
  },
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    {
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       "      <th>component</th>\n",
       "      <th>series_id</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>production_kb</td>\n",
       "      <td>MCRFPP31</td>\n",
       "      <td>Gulf Coast (PADD 3) Field Production of Crude ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>demand_kb</td>\n",
       "      <td>MCRRIP31</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>imports_kb</td>\n",
       "      <td>MCRIMP31</td>\n",
       "      <td>Gulf Coast (PADD 3) Imports of Crude Oil (Thou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>exports_kb</td>\n",
       "      <td>MCREXP31</td>\n",
       "      <td>Gulf Coast (PADD 3) Exports of Crude Oil (Thou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>net_receipts_kb</td>\n",
       "      <td>MCRNRP31</td>\n",
       "      <td>Gulf Coast (PADD 3) Net Receipts by Pipeline, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>adjustments_kb</td>\n",
       "      <td>MCRUA_R30_1</td>\n",
       "      <td>Gulf Coast (PADD 3) Supply Adjustment of Crude...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>transfers_kb</td>\n",
       "      <td>M_EPC0_TVP_R30_MBBL</td>\n",
       "      <td>Gulf Coast (PADD 3) Transfers to Crude Oil Sup...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>direct_use_kb</td>\n",
       "      <td>MCRUPP31</td>\n",
       "      <td>Gulf Coast (PADD 3) Product Supplied of Crude ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>commercial_stock_kb</td>\n",
       "      <td>MCESTP31</td>\n",
       "      <td>Gulf Coast (PADD 3) Ending Stocks excluding SP...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>cdu_capacity_kbd</td>\n",
       "      <td>MOCLEP32</td>\n",
       "      <td>Gulf Coast (PADD 3) Operable Crude Oil Distill...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              component            series_id  \\\n",
       "20        production_kb             MCRFPP31   \n",
       "21            demand_kb             MCRRIP31   \n",
       "22           imports_kb             MCRIMP31   \n",
       "23           exports_kb             MCREXP31   \n",
       "24      net_receipts_kb             MCRNRP31   \n",
       "25       adjustments_kb          MCRUA_R30_1   \n",
       "26         transfers_kb  M_EPC0_TVP_R30_MBBL   \n",
       "27        direct_use_kb             MCRUPP31   \n",
       "28  commercial_stock_kb             MCESTP31   \n",
       "29     cdu_capacity_kbd             MOCLEP32   \n",
       "\n",
       "                                                title  \n",
       "20  Gulf Coast (PADD 3) Field Production of Crude ...  \n",
       "21  Gulf Coast (PADD 3) Refinery and Blender Net I...  \n",
       "22  Gulf Coast (PADD 3) Imports of Crude Oil (Thou...  \n",
       "23  Gulf Coast (PADD 3) Exports of Crude Oil (Thou...  \n",
       "24  Gulf Coast (PADD 3) Net Receipts by Pipeline, ...  \n",
       "25  Gulf Coast (PADD 3) Supply Adjustment of Crude...  \n",
       "26  Gulf Coast (PADD 3) Transfers to Crude Oil Sup...  \n",
       "27  Gulf Coast (PADD 3) Product Supplied of Crude ...  \n",
       "28  Gulf Coast (PADD 3) Ending Stocks excluding SP...  \n",
       "29  Gulf Coast (PADD 3) Operable Crude Oil Distill...  "
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       "      <td>234</td>\n",
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       "          start        end  months\n",
       "padd                              \n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>component</th>\n",
       "      <th>exports_kbd_assumed_zero</th>\n",
       "      <th>transfers_kbd_assumed_zero</th>\n",
       "      <th>net_receipts_kbd_assumed_zero</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>padd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>103</td>\n",
       "      <td>180</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "component  exports_kbd_assumed_zero  transfers_kbd_assumed_zero  \\\n",
       "padd                                                              \n",
       "1                                21                         180   \n",
       "2                                 0                         180   \n",
       "3                                63                         180   \n",
       "4                                98                         180   \n",
       "5                               103                         180   \n",
       "\n",
       "component  net_receipts_kbd_assumed_zero  \n",
       "padd                                      \n",
       "1                                      0  \n",
       "2                                      0  \n",
       "3                                      0  \n",
       "4                                      0  \n",
       "5                                     43  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>component</th>\n",
       "      <th>padd</th>\n",
       "      <th>padd_name</th>\n",
       "      <th>month</th>\n",
       "      <th>cdu_capacity_kbd</th>\n",
       "      <th>demand_kbd</th>\n",
       "      <th>utilization_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>1</td>\n",
       "      <td>East Coast</td>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>928.00</td>\n",
       "      <td>727.00</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>2</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>4,283.00</td>\n",
       "      <td>4,291.17</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>3</td>\n",
       "      <td>Gulf Coast</td>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>9,888.00</td>\n",
       "      <td>9,525.30</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>4</td>\n",
       "      <td>Rocky Mountain</td>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>653.00</td>\n",
       "      <td>656.33</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>5</td>\n",
       "      <td>West Coast</td>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>2,275.00</td>\n",
       "      <td>2,000.47</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "component  padd       padd_name      month  cdu_capacity_kbd  demand_kbd  \\\n",
       "233           1      East Coast 2026-06-01            928.00      727.00   \n",
       "467           2         Midwest 2026-06-01          4,283.00    4,291.17   \n",
       "701           3      Gulf Coast 2026-06-01          9,888.00    9,525.30   \n",
       "935           4  Rocky Mountain 2026-06-01            653.00      656.33   \n",
       "1169          5      West Coast 2026-06-01          2,275.00    2,000.47   \n",
       "\n",
       "component  utilization_ratio  \n",
       "233                     0.78  \n",
       "467                     1.00  \n",
       "701                     0.96  \n",
       "935                     1.01  \n",
       "1169                    0.88  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Audit the loaded snapshot: show Gulf Coast source identifiers, each PADD's\n",
    "# calendar coverage, counts of assumed-zero observations, and latest refinery\n",
    "# input/capacity. Flags make missing-data assumptions visible before fitting.\n",
    "\n",
    "panel=load_panel(HERE/'data')\n",
    "manifest=pd.read_json(HERE/'data/source_manifest.json')\n",
    "display(manifest[manifest.padd.eq(3)][['component','series_id','title']])\n",
    "display(panel.groupby('padd').agg(start=('month','min'),end=('month','max'),months=('month','size')))\n",
    "flags=[c for c in panel if c.endswith('_assumed_zero')]\n",
    "display(panel.groupby('padd')[flags].sum())\n",
    "display(panel.sort_values('month').groupby('padd').tail(1)[\n",
    "    ['padd','padd_name','month','cdu_capacity_kbd','demand_kbd','utilization_ratio']].sort_values('padd'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f2a534c",
   "metadata": {},
   "source": [
    "Blank exports and net receipts are assumed zero and flagged. Transfers before January 2022 are set to zero because the reporting category had not started. Withheld and unavailable values remain missing. All five PADDs must have a complete, common monthly calendar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b06058b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:47.151081Z",
     "iopub.status.busy": "2026-09-16T22:03:47.150968Z",
     "iopub.status.idle": "2026-09-16T22:03:47.326713Z",
     "shell.execute_reply": "2026-09-16T22:03:47.326068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot commercial inventories, excluding SPR, at national and regional scales.\n",
    "# Stocks are stored in thousand barrels; dividing by 1000 gives million barrels.\n",
    "\n",
    "national_history=panel.groupby('month')[['stock_kb']].sum()\n",
    "fig,axes=plt.subplots(1,2,figsize=(14,4))\n",
    "(national_history/1000).rename(columns={'stock_kb':'Commercial crude (target)'}).plot(ax=axes[0])\n",
    "for p,g in panel.groupby('padd'):\n",
    "    axes[1].plot(g.month,g.stock_kb/1000,label=f'PADD {p}')\n",
    "axes[0].set_title('Commercial crude inventory target');axes[1].set_title('Commercial crude by PADD');axes[1].legend()\n",
    "for ax in axes:ax.set_ylabel('Million barrels')\n",
    "plt.tight_layout();plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b56076",
   "metadata": {},
   "source": [
    "## Model\n",
    "\n",
    "Estimate how supply and disposition affect commercial stocks, then forecast each PADD and sum to the U.S. total.\n",
    "\n",
    "**Monthly stock change = days × (production + imports + net receipts + adjustments + transfers − refinery inputs − exports − direct use + residual).**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cf3bbef",
   "metadata": {},
   "source": [
    "The residual measures the difference between reported net flows and commercial stock changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48d56788",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:47.327989Z",
     "iopub.status.busy": "2026-09-16T22:03:47.327842Z",
     "iopub.status.idle": "2026-09-16T22:03:47.490758Z",
     "shell.execute_reply": "2026-09-16T22:03:47.490337Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>incomplete_balance_mae_kbd</th>\n",
       "      <th>complete_balance_mae_kbd</th>\n",
       "      <th>max_complete_residual_kbd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>padd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>161.80</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>825.28</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1,198.28</td>\n",
       "      <td>91.67</td>\n",
       "      <td>1,262.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>462.47</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>146.96</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      incomplete_balance_mae_kbd  complete_balance_mae_kbd  \\\n",
       "padd                                                         \n",
       "1                         161.80                      0.01   \n",
       "2                         825.28                      0.02   \n",
       "3                       1,198.28                     91.67   \n",
       "4                         462.47                      0.01   \n",
       "5                         146.96                      0.01   \n",
       "\n",
       "      max_complete_residual_kbd  \n",
       "padd                             \n",
       "1                          0.04  \n",
       "2                          1.94  \n",
       "3                      1,262.68  \n",
       "4                          0.19  \n",
       "5                          0.04  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>component</th>\n",
       "      <th>month</th>\n",
       "      <th>padd</th>\n",
       "      <th>accounting_residual_kbd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>2007-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-26.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-28.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>2007-08-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-5.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>2007-09-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-78.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>2007-10-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-42.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>2026-02-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-8.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>2026-03-01</td>\n",
       "      <td>3</td>\n",
       "      <td>19.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>676.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>2026-05-01</td>\n",
       "      <td>3</td>\n",
       "      <td>1,262.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>3</td>\n",
       "      <td>1,106.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "component      month  padd  accounting_residual_kbd\n",
       "471       2007-04-01     3                   -26.33\n",
       "472       2007-05-01     3                   -28.32\n",
       "475       2007-08-01     3                    -5.39\n",
       "476       2007-09-01     3                   -78.87\n",
       "477       2007-10-01     3                   -42.77\n",
       "..               ...   ...                      ...\n",
       "697       2026-02-01     3                    -8.18\n",
       "698       2026-03-01     3                    19.90\n",
       "699       2026-04-01     3                   676.73\n",
       "700       2026-05-01     3                 1,262.68\n",
       "701       2026-06-01     3                 1,106.47\n",
       "\n",
       "[128 rows x 3 columns]"
      ]
     },
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     "output_type": "display_data"
    },
    {
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",
      "text/plain": [
       "<Figure size 1400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare residuals from the four-flow shortcut and the full published-flow\n",
    "# balance. The >2 kbd exception threshold is a diagnostic display rule, not a\n",
    "# training filter or a rule for forcing the commercial residual to zero.\n",
    "\n",
    "audit=panel.groupby('padd').agg(\n",
    "    incomplete_balance_mae_kbd=('five_term_residual_kbd',lambda s:s.abs().mean()),\n",
    "    complete_balance_mae_kbd=('accounting_residual_kbd',lambda s:s.abs().mean()),\n",
    "    max_complete_residual_kbd=('accounting_residual_kbd',lambda s:s.abs().max()))\n",
    "display(audit)\n",
    "exceptions=panel[panel.accounting_residual_kbd.abs()>2]\n",
    "display(exceptions[['month','padd','accounting_residual_kbd']])\n",
    "fig,axes=plt.subplots(1,2,figsize=(14,3))\n",
    "for p,g in panel.groupby('padd'):\n",
    "    axes[0].plot(g.month,g.five_term_residual_kbd,label=f'PADD {p}',alpha=.7)\n",
    "    axes[1].plot(g.month,g.accounting_residual_kbd,label=f'PADD {p}',alpha=.7)\n",
    "axes[0].set(title='Four-flow commercial balance residual',ylabel='Thousand barrels/day')\n",
    "axes[1].set(title='Published-flow commercial balance residual',ylabel='Thousand barrels/day')\n",
    "axes[0].legend(ncol=3,fontsize=8);plt.tight_layout();plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe55ea46",
   "metadata": {},
   "source": [
    "### Forecast inputs\n",
    "\n",
    "Predict month-end stocks using observations through the prior month and seasonal patterns. Forecast flows from trends and calendar-month effects fitted to the latest 60 eligible observations. Refinery inputs equal forecast utilization times capacity held at the forecast origin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24d813ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:47.492030Z",
     "iopub.status.busy": "2026-09-16T22:03:47.491898Z",
     "iopub.status.idle": "2026-09-16T22:03:47.495336Z",
     "shell.execute_reply": "2026-09-16T22:03:47.494874Z"
    }
   },
   "outputs": [],
   "source": [
    "# Build a trend in years plus 11 calendar-month indicators. January is the\n",
    "# reference month represented by the regression intercept.\n",
    "def seasonal_design(months, origin):\n",
    "    dates = pd.DatetimeIndex(months)\n",
    "    trend = ((dates.year - origin.year) * 12 + dates.month - origin.month).to_numpy() / 12\n",
    "    return np.column_stack([trend] + [(dates.month == m).astype(float) for m in range(2, 13)])\n",
    "\n",
    "\n",
    "# Use only the supplied origin history. The last 60 non-COVID observations may\n",
    "# span more than 60 calendar months because excluded months are skipped.\n",
    "def forecast_flows(history, months):\n",
    "    \"\"\"Forecast daily flows and crude-input/capacity ratio from origin history.\"\"\"\n",
    "    train = history.loc[outside_covid(history.month)].tail(60)\n",
    "    # For crude, model refinery utilization instead of demand directly, then recover\n",
    "    # refinery input using utilization times capacity known at the forecast origin.\n",
    "    targets = [c for c in FLOWS if c != 'demand_kbd'] + ['utilization_ratio']\n",
    "    model = LinearRegression().fit(\n",
    "        seasonal_design(train.month, train.month.iloc[0]), train[targets])\n",
    "    result = pd.DataFrame(model.predict(seasonal_design(months, train.month.iloc[0])),\n",
    "                          columns=targets, index=pd.DatetimeIndex(months))\n",
    "    # Clip forecast utilization to 0-100% and freeze capacity at its last known\n",
    "    # value; future actual capacity must not enter a historical forecast.\n",
    "    result['utilization_ratio'] = result.utilization_ratio.clip(0, 1)\n",
    "    result['cdu_capacity_kbd'] = float(history.cdu_capacity_kbd.iloc[-1])\n",
    "    result['demand_kbd'] = result.utilization_ratio * result.cdu_capacity_kbd\n",
    "    for c in ['production_kbd', 'imports_kbd', 'exports_kbd', 'direct_use_kbd']:\n",
    "        result[c] = result[c].clip(lower=0)\n",
    "    return result[FLOWS + ['cdu_capacity_kbd', 'utilization_ratio']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b89dad0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:47.496449Z",
     "iopub.status.busy": "2026-09-16T22:03:47.496350Z",
     "iopub.status.idle": "2026-09-16T22:03:47.500113Z",
     "shell.execute_reply": "2026-09-16T22:03:47.499705Z"
    }
   },
   "outputs": [],
   "source": [
    "# Build target-month inputs from history ending one month earlier. History must\n",
    "# be sorted, continuous, and contain at least 12 monthly observations.\n",
    "def feature_row(history, month):\n",
    "    last = history.iloc[-1]\n",
    "    # Use prior stock, year-ago stock, and the latest stock change. Sine/cosine encode\n",
    "    # annual seasonality smoothly across the December/January boundary.\n",
    "    row = {'stock_lag1': last.stock_kb, 'stock_lag12': history.iloc[-12].stock_kb,\n",
    "           'change_lag1': last.stock_kb - history.iloc[-2].stock_kb,\n",
    "           'sin_month': np.sin(2*np.pi*month.month/12), 'cos_month': np.cos(2*np.pi*month.month/12)}\n",
    "    # Calculate observed changes before filtering; omit any change whose current\n",
    "    # or previous stock observation falls inside the excluded COVID period.\n",
    "    changes = history.set_index('month').stock_kb.diff()\n",
    "    clean = outside_covid(changes.index) & outside_covid(changes.index - pd.DateOffset(months=1))\n",
    "    changes = changes.loc[clean].tail(60)\n",
    "    # Average recent eligible changes for the target calendar month; use zero\n",
    "    # if that month has no usable historical changes.\n",
    "    seasonal = changes[changes.index.month == month.month].dropna()\n",
    "    row['seasonal_change'] = float(seasonal.mean()) if len(seasonal) else 0.0\n",
    "    # Compare the latest stock with earlier same-calendar-month stock levels,\n",
    "    # excluding the latest observation from its own reference average.\n",
    "    normal_history = history.iloc[:-1].loc[lambda f: outside_covid(f.month)].tail(60)\n",
    "    normal = normal_history[normal_history.month.dt.month.eq(last.month.month)].stock_kb\n",
    "    row['stock_seasonal_gap'] = float(last.stock_kb - normal.mean()) if len(normal) else 0.0\n",
    "    # Pre-sign prior-month flows so nonnegative regression coefficients retain the\n",
    "    # supply-versus-demand direction in the constrained model.\n",
    "    row.update({'lag_' + c: last[c] * sign for c, sign in zip(FLOWS, SIGNS)})\n",
    "    return row\n",
    "\n",
    "\n",
    "# Create labeled rows after a 12-month warm-up. Rebuild each feature vector and\n",
    "# flow-identity benchmark using only history preceding that row's target month.\n",
    "def supervised(history):\n",
    "    rows = []\n",
    "    for i in range(12, len(history)):\n",
    "        # Keep the target separate: its stock supplies actual_kb/delta_kb only. Actual\n",
    "        # target-month flows must not become inputs to a prediction of that month.\n",
    "        past, current = history.iloc[:i], history.iloc[i]\n",
    "        row = feature_row(past, current.month)\n",
    "        flow = forecast_flows(past, [current.month]).iloc[0]\n",
    "        row.update(month=current.month, origin_month=past.month.iloc[-1],\n",
    "                   actual_kb=current.stock_kb, delta_kb=current.stock_kb-past.stock_kb.iloc[-1],\n",
    "                   forecast_flow_identity=max(0.0, past.stock_kb.iloc[-1] + flow[FLOWS].to_numpy() @ SIGNS * current.month.days_in_month))\n",
    "        rows.append(row)\n",
    "    return pd.DataFrame(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2133388c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:47.501024Z",
     "iopub.status.busy": "2026-09-16T22:03:47.500926Z",
     "iopub.status.idle": "2026-09-16T22:03:50.804339Z",
     "shell.execute_reply": "2026-09-16T22:03:50.803929Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin_month</th>\n",
       "      <th>month</th>\n",
       "      <th>stock_lag1</th>\n",
       "      <th>lag_production_kbd</th>\n",
       "      <th>lag_demand_kbd</th>\n",
       "      <th>forecast_flow_identity</th>\n",
       "      <th>actual_kb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>2026-01-01</td>\n",
       "      <td>2026-02-01</td>\n",
       "      <td>224,301.00</td>\n",
       "      <td>9,849.23</td>\n",
       "      <td>-8,907.68</td>\n",
       "      <td>243,036.16</td>\n",
       "      <td>249,711.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>2026-02-01</td>\n",
       "      <td>2026-03-01</td>\n",
       "      <td>249,711.00</td>\n",
       "      <td>10,211.14</td>\n",
       "      <td>-8,733.11</td>\n",
       "      <td>260,609.30</td>\n",
       "      <td>260,536.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>2026-03-01</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>260,536.00</td>\n",
       "      <td>10,190.74</td>\n",
       "      <td>-9,276.29</td>\n",
       "      <td>269,918.32</td>\n",
       "      <td>258,758.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>2026-05-01</td>\n",
       "      <td>258,758.00</td>\n",
       "      <td>10,439.80</td>\n",
       "      <td>-9,360.27</td>\n",
       "      <td>261,554.91</td>\n",
       "      <td>244,403.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>2026-05-01</td>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>244,403.00</td>\n",
       "      <td>10,221.13</td>\n",
       "      <td>-9,556.10</td>\n",
       "      <td>240,283.49</td>\n",
       "      <td>233,275.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    origin_month      month  stock_lag1  lag_production_kbd  lag_demand_kbd  \\\n",
       "217   2026-01-01 2026-02-01  224,301.00            9,849.23       -8,907.68   \n",
       "218   2026-02-01 2026-03-01  249,711.00           10,211.14       -8,733.11   \n",
       "219   2026-03-01 2026-04-01  260,536.00           10,190.74       -9,276.29   \n",
       "220   2026-04-01 2026-05-01  258,758.00           10,439.80       -9,360.27   \n",
       "221   2026-05-01 2026-06-01  244,403.00           10,221.13       -9,556.10   \n",
       "\n",
       "     forecast_flow_identity  actual_kb  \n",
       "217              243,036.16 249,711.00  \n",
       "218              260,609.30 260,536.00  \n",
       "219              269,918.32 258,758.00  \n",
       "220              261,554.91 244,403.00  \n",
       "221              240,283.49 233,275.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect a few supervised rows to make the forecast origin/target alignment\n",
    "# concrete. Each origin must precede its target; actual_kb is an outcome only.\n",
    "\n",
    "example=supervised(panel[panel.padd.eq(3)].reset_index(drop=True))\n",
    "display(example[['origin_month','month','stock_lag1','lag_production_kbd','lag_demand_kbd',\n",
    "                 'forecast_flow_identity','actual_kb']].tail())\n",
    "assert (example.origin_month<example.month).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c38743a2",
   "metadata": {},
   "source": [
    "### Tested models\n",
    "\n",
    "Compare 17 methods: unchanged stocks, prior-year stocks, seasonal changes, flow balance, regression variants, random forest, XGBoost, a neural network, and an ensemble. The definitions below specify their inputs and settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "53006211",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:50.806112Z",
     "iopub.status.busy": "2026-09-16T22:03:50.806006Z",
     "iopub.status.idle": "2026-09-16T22:03:50.810528Z",
     "shell.execute_reply": "2026-09-16T22:03:50.810194Z"
    }
   },
   "outputs": [],
   "source": [
    "# Baselines carry stocks forward, repeat year-ago stocks, add a seasonal change,\n",
    "# or accumulate forecast net flows; none needs a fitted stock estimator.\n",
    "BASELINES = ['persistence', 'seasonal_naive', 'seasonal_change', 'forecast_flow_identity']\n",
    "# Candidates share forecast timing and evaluation. Names ending in '_change'\n",
    "# model stock increments, while constrained_level models inventory levels.\n",
    "LEARNED = ['constrained_level', 'ridge_change', 'seasonal_ridge_change',\n",
    "           'polynomial_ridge_change', 'spline_ridge_change', 'random_forest_change',\n",
    "           'xgboost_change', 'neural_network_change', 'rolling_ridge_change',\n",
    "           'damped_ridge_change', 'ensemble_change']\n",
    "LEARNED += ['seasonal_residual_change', 'huber_change']\n",
    "MODELS = BASELINES + LEARNED\n",
    "BASE_FEATURES = ['stock_lag1', 'stock_lag12', 'change_lag1'] + ['lag_' + c for c in FLOWS]\n",
    "FEATURES = BASE_FEATURES + ['sin_month', 'cos_month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6067692d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:50.812094Z",
     "iopub.status.busy": "2026-09-16T22:03:50.811692Z",
     "iopub.status.idle": "2026-09-16T22:03:50.816573Z",
     "shell.execute_reply": "2026-09-16T22:03:50.816244Z"
    }
   },
   "outputs": [],
   "source": [
    "# Select predictor columns explicitly. actual_kb and delta_kb are labels and\n",
    "# must never enter the estimator's input matrix.\n",
    "def columns(name):\n",
    "    if name == 'seasonal_residual_change':\n",
    "        return FEATURES + ['stock_seasonal_gap', 'seasonal_change']\n",
    "    if name == 'constrained_level':\n",
    "        return ['stock_lag1'] + ['lag_' + c for c in FLOWS]\n",
    "    if name == 'ridge_change':\n",
    "        return BASE_FEATURES\n",
    "    return FEATURES\n",
    "\n",
    "\n",
    "# Return an unfitted candidate. Pipeline transformations are learned inside each\n",
    "# fit, so test observations cannot influence scaling or spline preparation.\n",
    "def estimator(name):\n",
    "    if name == 'seasonal_residual_change':\n",
    "        # Standardize predictors before L2-regularized regression. Larger alpha shrinks\n",
    "        # coefficients more strongly; grid search overrides these starting defaults.\n",
    "        return make_pipeline(StandardScaler(), Ridge(alpha=100))\n",
    "    if name == 'huber_change':\n",
    "        return TransformedTargetRegressor(regressor=make_pipeline(StandardScaler(),\n",
    "            # Huber loss reduces outlier influence; the surrounding target transformer\n",
    "            # scales the response and reverses that scaling when predicting.\n",
    "            HuberRegressor(max_iter=1000)), transformer=StandardScaler())\n",
    "    if name == 'constrained_level':\n",
    "        # Constrain coefficients, not the intercept, to be nonnegative. Pre-signed demand\n",
    "        # features therefore contribute negatively without imposing a full flow identity.\n",
    "        return LinearRegression(positive=True)\n",
    "    if name in ['ridge_change', 'seasonal_ridge_change', 'rolling_ridge_change', 'damped_ridge_change']:\n",
    "        # Standardize predictors before L2-regularized regression. Larger alpha shrinks\n",
    "        # coefficients more strongly; grid search overrides these starting defaults.\n",
    "        return make_pipeline(StandardScaler(), Ridge(alpha=100))\n",
    "    if name == 'polynomial_ridge_change':\n",
    "        # Expand predictors with powers/interactions, rescale, and regularize to control\n",
    "        # flexibility. The regression supplies the intercept, so no bias column is needed.\n",
    "        return make_pipeline(StandardScaler(), PolynomialFeatures(2, include_bias=False),\n",
    "                             StandardScaler(), Ridge(alpha=1000))\n",
    "    if name == 'spline_ridge_change':\n",
    "        # Represent each feature using smooth piecewise-polynomial basis functions;\n",
    "        # linear extrapolation controls behavior outside its observed training range.\n",
    "        return make_pipeline(SplineTransformer(n_knots=4, degree=2, extrapolation='linear'),\n",
    "                             StandardScaler(), Ridge(alpha=100))\n",
    "    if name == 'random_forest_change':\n",
    "        # Average resampled decision trees. Depth and minimum leaf size limit complexity;\n",
    "        # the fixed seed makes randomized fitting reproducible.\n",
    "        return RandomForestRegressor(n_estimators=150, max_depth=4, min_samples_leaf=12,\n",
    "                                     max_features=0.8, n_jobs=1, random_state=42)\n",
    "    if name == 'xgboost_change':\n",
    "        # Boost shallow trees sequentially. Learning rate, regularization, and row/column\n",
    "        # subsampling control how aggressively the ensemble fits remaining residuals.\n",
    "        return XGBRegressor(n_estimators=120, max_depth=2, learning_rate=0.03,\n",
    "            min_child_weight=12, reg_lambda=30, subsample=0.8, colsample_bytree=0.8,\n",
    "            n_jobs=1, random_state=42, objective='reg:squarederror')\n",
    "    if name == 'neural_network_change':\n",
    "        return TransformedTargetRegressor(regressor=make_pipeline(StandardScaler(),\n",
    "            # Fit a small neural network with LBFGS on scaled inputs and a scaled target.\n",
    "            # Alpha penalizes weights, and the random seed fixes initialization.\n",
    "            MLPRegressor(hidden_layer_sizes=(16,), alpha=10, solver='lbfgs',\n",
    "                         max_iter=3000, random_state=42)), transformer=StandardScaler())\n",
    "    if name == 'ensemble_change':\n",
    "        # Average seasonal ridge, random forest, and XGBoost predictions. The tuning\n",
    "        # grid varies their relative weights.\n",
    "        return VotingRegressor([(n, estimator(n)) for n in\n",
    "            ['seasonal_ridge_change', 'random_forest_change', 'xgboost_change']])\n",
    "    raise ValueError(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d033e6ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:50.818206Z",
     "iopub.status.busy": "2026-09-16T22:03:50.817799Z",
     "iopub.status.idle": "2026-09-16T22:03:50.825322Z",
     "shell.execute_reply": "2026-09-16T22:03:50.824892Z"
    }
   },
   "outputs": [],
   "source": [
    "# Return (estimator, warning_text). Baselines return None because predict computes\n",
    "# them directly; learned candidates filter training dates before fitting.\n",
    "def fit(name, train, params=None):\n",
    "    if name in BASELINES:\n",
    "        return None, ''\n",
    "    train = training_rows(train)\n",
    "    model = estimator(name)\n",
    "    if params:\n",
    "        model.set_params(**params)\n",
    "    if name == 'rolling_ridge_change':\n",
    "        train = train.tail(60)\n",
    "    # Choose inventory level or monthly change as response, both in thousand barrels.\n",
    "    target = 'actual_kb' if name == 'constrained_level' else 'delta_kb'\n",
    "    # Match the search's numerical settings, including neural-network refits.\n",
    "    from threadpoolctl import threadpool_limits\n",
    "    with warnings.catch_warnings(record=True) as caught, threadpool_limits(limits=1):\n",
    "        warnings.simplefilter('always', ConvergenceWarning)\n",
    "        # Fit the seasonal-residual candidate to departures from normal seasonal change;\n",
    "        # predict reconstructs the seasonal component before converting to stock levels.\n",
    "        values = train[target] - train.seasonal_change if name == 'seasonal_residual_change' else train[target]\n",
    "        model.fit(train[columns(name)], values)\n",
    "    return model, '; '.join(str(w.message) for w in caught)\n",
    "\n",
    "\n",
    "# Return stock levels in kb for every candidate, including change-based models.\n",
    "# Use identical reconstruction and flooring rules during tuning and forecasting.\n",
    "def predict(name, model, frame):\n",
    "    if name == 'persistence':\n",
    "        return frame.stock_lag1.to_numpy()\n",
    "    if name == 'seasonal_naive':\n",
    "        return frame.stock_lag12.to_numpy()\n",
    "    if name == 'seasonal_change':\n",
    "        return np.maximum(0, frame.stock_lag1.to_numpy() + frame.seasonal_change.to_numpy())\n",
    "    if name == 'forecast_flow_identity':\n",
    "        return frame.forecast_flow_identity.to_numpy()\n",
    "    prediction = model.predict(frame[columns(name)])\n",
    "    if name == 'seasonal_residual_change':\n",
    "        prediction += frame.seasonal_change.to_numpy()\n",
    "    # Halve the estimated change to shrink the forecast toward unchanged stocks.\n",
    "    if name == 'damped_ridge_change':\n",
    "        prediction *= 0.5\n",
    "    # Add the predicted increment to prior stock to recover an inventory level.\n",
    "    if name != 'constrained_level':\n",
    "        prediction += frame.stock_lag1.to_numpy()\n",
    "    # Prevent a learned model from producing a negative final stock level.\n",
    "    return np.maximum(prediction, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "07b43d9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:50.826747Z",
     "iopub.status.busy": "2026-09-16T22:03:50.826624Z",
     "iopub.status.idle": "2026-09-16T22:03:50.834564Z",
     "shell.execute_reply": "2026-09-16T22:03:50.834183Z"
    }
   },
   "outputs": [],
   "source": [
    "# Enumerate tunable combinations. Double underscores address nested pipeline\n",
    "# parameters; an empty grid indicates a candidate with no parameter search.\n",
    "def parameter_grid(name):\n",
    "    \"\"\"Conventional, compact Cartesian grids; structural baselines stay fixed.\"\"\"\n",
    "    if name == 'seasonal_residual_change':\n",
    "        return {'ridge__alpha': [0.1, 1, 10, 100, 1000]}\n",
    "    if name == 'huber_change':\n",
    "        return {'regressor__huberregressor__epsilon': [1.35, 1.75],\n",
    "                'regressor__huberregressor__alpha': [0.01, 1, 10]}\n",
    "    if name in ['ridge_change', 'seasonal_ridge_change', 'rolling_ridge_change', 'damped_ridge_change']:\n",
    "        return {'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}\n",
    "    if name == 'polynomial_ridge_change':\n",
    "        return {'polynomialfeatures__degree': [1, 2], 'ridge__alpha': [1, 10, 100, 1000]}\n",
    "    if name == 'spline_ridge_change':\n",
    "        return {'splinetransformer__n_knots': [3, 5, 7],\n",
    "                'splinetransformer__degree': [2, 3], 'ridge__alpha': [1, 100, 1000]}\n",
    "    if name == 'random_forest_change':\n",
    "        return {'n_estimators': [100, 200], 'max_depth': [3, 5, None],\n",
    "                'min_samples_leaf': [5, 12]}\n",
    "    if name == 'xgboost_change':\n",
    "        return {'n_estimators': [100, 200], 'max_depth': [2, 4],\n",
    "                'learning_rate': [0.01, 0.05, 0.1], 'reg_lambda': [1, 30]}\n",
    "    if name == 'neural_network_change':\n",
    "        return {'regressor__mlpregressor__hidden_layer_sizes': [(16,), (32,), (32, 16)],\n",
    "                'regressor__mlpregressor__alpha': [0.01, 1, 10]}\n",
    "    if name == 'ensemble_change':\n",
    "        return {'weights': [(1, 1, 1), (2, 1, 1), (1, 2, 1), (1, 1, 2)]}\n",
    "    return {}\n",
    "\n",
    "\n",
    "# Adapt candidates to sklearn's cloning/search interface. Score reconstructed\n",
    "# stock levels, including seasonal adjustments, damping, and the zero floor.\n",
    "class StockRegressor(RegressorMixin, BaseEstimator):\n",
    "    \"\"\"Score the actual stock forecast, including damping, floor and rolling fit.\"\"\"\n",
    "    def __init__(self, name, model):\n",
    "        self.name = name\n",
    "        self.model = model\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        from sklearn.base import clone\n",
    "        # The sliced frame carries labels and dates for eligibility filtering; y is\n",
    "        # accepted for sklearn compatibility, while the response is selected below.\n",
    "        eligible = training_rows(X)\n",
    "        train = eligible.tail(60) if self.name == 'rolling_ridge_change' else eligible\n",
    "        target = train.actual_kb if self.name == 'constrained_level' else train.delta_kb\n",
    "        if self.name == 'seasonal_residual_change':\n",
    "            target = target - train.seasonal_change\n",
    "        # Clone an unfitted candidate for each fold so fitted state cannot leak across\n",
    "        # search trials. A trailing underscore conventionally marks fitted attributes.\n",
    "        self.model_ = clone(self.model).fit(train[columns(self.name)], target)\n",
    "        return self\n",
    "\n",
    "    def predict(self, X):\n",
    "        return predict(self.name, self.model_, X)\n",
    "\n",
    "\n",
    "# Search only the supplied past window; return parameters, all candidate/fold\n",
    "# scores, and convergence warnings. The chosen estimator is fitted separately.\n",
    "def tune(name, train, folds=10, jobs=8, splits=None):\n",
    "    \"\"\"Search only the supplied past history; retain every candidate and fold score.\"\"\"\n",
    "    grid = parameter_grid(name)\n",
    "    if not grid:\n",
    "        return {}, pd.DataFrame(), ''\n",
    "    cv = splits if splits is not None else validation_splits(train, folds)\n",
    "    # Maximize negative MAE, equivalent to minimizing stock error. model__ reaches\n",
    "    # the candidate inside StockRegressor; refit=False avoids an unused final fit.\n",
    "    search = GridSearchCV(StockRegressor(name, estimator(name)),\n",
    "        {'model__' + key: value for key, value in grid.items()},\n",
    "        scoring='neg_mean_absolute_error', cv=cv, n_jobs=jobs,\n",
    "        refit=False, error_score='raise', return_train_score=True)\n",
    "    # Threads keep warnings in this process; native numerical threads are capped.\n",
    "    from joblib import parallel_backend\n",
    "    from threadpoolctl import threadpool_limits\n",
    "    with warnings.catch_warnings(record=True) as caught, threadpool_limits(limits=1), parallel_backend('threading'):\n",
    "        warnings.simplefilter('always', ConvergenceWarning)\n",
    "        search.fit(train, train.actual_kb)\n",
    "    results = pd.DataFrame(search.cv_results_)\n",
    "    results['params'] = results.params.map(lambda p: json.dumps({k.removeprefix('model__'): v for k,v in p.items()}, sort_keys=True))\n",
    "    # Reverse sklearn's negative-score convention to report positive MAE.\n",
    "    results['mean_test_mae_kb'] = -results.mean_test_score\n",
    "    # Remove the wrapper prefix so fit() can set parameters on the candidate itself.\n",
    "    params = {k.removeprefix('model__'): v for k,v in search.best_params_.items()}\n",
    "    warning = '; '.join(sorted({str(w.message) for w in caught}))\n",
    "    return params, results, warning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "47a65eff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:50.836117Z",
     "iopub.status.busy": "2026-09-16T22:03:50.835761Z",
     "iopub.status.idle": "2026-09-16T22:03:50.853116Z",
     "shell.execute_reply": "2026-09-16T22:03:50.852794Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>param_grid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>constrained_level</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ridge_change</td>\n",
       "      <td>{'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>seasonal_ridge_change</td>\n",
       "      <td>{'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>polynomial_ridge_change</td>\n",
       "      <td>{'polynomialfeatures__degree': [1, 2], 'ridge_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>spline_ridge_change</td>\n",
       "      <td>{'splinetransformer__n_knots': [3, 5, 7], 'spl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>{'n_estimators': [100, 200], 'max_depth': [3, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>{'n_estimators': [100, 200], 'max_depth': [2, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>{'regressor__mlpregressor__hidden_layer_sizes'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>rolling_ridge_change</td>\n",
       "      <td>{'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>damped_ridge_change</td>\n",
       "      <td>{'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ensemble_change</td>\n",
       "      <td>{'weights': [(1, 1, 1), (2, 1, 1), (1, 2, 1), ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>{'ridge__alpha': [0.1, 1, 10, 100, 1000]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>huber_change</td>\n",
       "      <td>{'regressor__huberregressor__epsilon': [1.35, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       model  \\\n",
       "0          constrained_level   \n",
       "1               ridge_change   \n",
       "2      seasonal_ridge_change   \n",
       "3    polynomial_ridge_change   \n",
       "4        spline_ridge_change   \n",
       "5       random_forest_change   \n",
       "6             xgboost_change   \n",
       "7      neural_network_change   \n",
       "8       rolling_ridge_change   \n",
       "9        damped_ridge_change   \n",
       "10           ensemble_change   \n",
       "11  seasonal_residual_change   \n",
       "12              huber_change   \n",
       "\n",
       "                                           param_grid  \n",
       "0                                                  {}  \n",
       "1     {'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}  \n",
       "2     {'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}  \n",
       "3   {'polynomialfeatures__degree': [1, 2], 'ridge_...  \n",
       "4   {'splinetransformer__n_knots': [3, 5, 7], 'spl...  \n",
       "5   {'n_estimators': [100, 200], 'max_depth': [3, ...  \n",
       "6   {'n_estimators': [100, 200], 'max_depth': [2, ...  \n",
       "7   {'regressor__mlpregressor__hidden_layer_sizes'...  \n",
       "8     {'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}  \n",
       "9     {'ridge__alpha': [0.01, 0.1, 1, 10, 100, 1000]}  \n",
       "10  {'weights': [(1, 1, 1), (2, 1, 1), (1, 2, 1), ...  \n",
       "11          {'ridge__alpha': [0.1, 1, 10, 100, 1000]}  \n",
       "12  {'regressor__huberregressor__epsilon': [1.35, ...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show each candidate's search space before running the experiment; nested\n",
    "# parameter names identify the relevant estimator or pipeline transformation.\n",
    "\n",
    "display(pd.DataFrame([{'model':name,'param_grid':parameter_grid(name)} for name in LEARNED]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6724564f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:50.854610Z",
     "iopub.status.busy": "2026-09-16T22:03:50.854473Z",
     "iopub.status.idle": "2026-09-16T22:03:50.862363Z",
     "shell.execute_reply": "2026-09-16T22:03:50.862023Z"
    }
   },
   "outputs": [],
   "source": [
    "# MAE and RMSE are in kb; RMSE weights large misses more. R-squared is unitless\n",
    "# and may be negative when errors exceed the actual sample's mean-only benchmark.\n",
    "def score(actual, predicted):\n",
    "    return {'mae_kb': mean_absolute_error(actual, predicted),\n",
    "            'rmse_kb': mean_squared_error(actual, predicted)**0.5,\n",
    "            'r2': r2_score(actual, predicted), 'n': len(actual)}\n",
    "\n",
    "\n",
    "# Require one row for every PADD in each group before summing additive columns.\n",
    "# Compute national errors after summation; regional forecast errors can offset.\n",
    "def aggregate(frame, keys, additive):\n",
    "    \"\"\"Reject partial U.S. totals instead of silently summing fewer than five PADDs.\"\"\"\n",
    "    if frame.duplicated(keys + ['padd']).any():\n",
    "        raise ValueError('Duplicate PADD rows in aggregation')\n",
    "    if not frame.groupby(keys).padd.nunique().eq(5).all():\n",
    "        raise ValueError('U.S. aggregation requires all five PADDs')\n",
    "    return frame.groupby(keys, as_index=False)[additive].sum(min_count=5)\n",
    "\n",
    "\n",
    "# Score each group, expanding either a scalar or tuple group key into the named\n",
    "# key columns of the resulting metrics table.\n",
    "def metric_table(predictions, keys):\n",
    "    return pd.DataFrame([{**dict(zip(keys, k if isinstance(k, tuple) else (k,))),\n",
    "        **score(g.actual_kb, g.predicted_kb)} for k,g in predictions.groupby(keys)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6bde4b41",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:50.865143Z",
     "iopub.status.busy": "2026-09-16T22:03:50.863621Z",
     "iopub.status.idle": "2026-09-16T22:03:50.882982Z",
     "shell.execute_reply": "2026-09-16T22:03:50.882250Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete constrained features: ['stock_lag1', 'lag_production_kbd', 'lag_demand_kbd', 'lag_imports_kbd', 'lag_exports_kbd', 'lag_net_receipts_kbd', 'lag_adjustments_kbd', 'lag_transfers_kbd', 'lag_direct_use_kbd']\n",
      "TransformedTargetRegressor(regressor=Pipeline(steps=[('standardscaler',\n",
      "                                                      StandardScaler()),\n",
      "                                                     ('mlpregressor',\n",
      "                                                      MLPRegressor(alpha=10,\n",
      "                                                                   hidden_layer_sizes=(16,),\n",
      "                                                                   max_iter=3000,\n",
      "                                                                   random_state=42,\n",
      "                                                                   solver='lbfgs'))]),\n",
      "                           transformer=StandardScaler())\n",
      "VotingRegressor(estimators=[('seasonal_ridge_change',\n",
      "                             Pipeline(steps=[('standardscaler',\n",
      "                                              StandardScaler()),\n",
      "                                             ('ridge', Ridge(alpha=100))])),\n",
      "                            ('random_forest_change',\n",
      "                             RandomForestRegressor(max_depth=4,\n",
      "                                                   max_features=0.8,\n",
      "                                                   min_samples_leaf=12,\n",
      "                                                   n_estimators=150, n_jobs=1,\n",
      "                                                   random_state=42)),\n",
      "                            ('xgboost_change',\n",
      "                             XGBRegressor(base_score=None, booster=None,\n",
      "                                          callbacks=None,\n",
      "                                          c...\n",
      "                                          feature_weights=None, gamma=None,\n",
      "                                          grow_policy=None,\n",
      "                                          importance_type=None,\n",
      "                                          interaction_constraints=None,\n",
      "                                          learning_rate=0.03, max_bin=None,\n",
      "                                          max_cat_threshold=None,\n",
      "                                          max_cat_to_onehot=None,\n",
      "                                          max_delta_step=None, max_depth=2,\n",
      "                                          max_leaves=None, min_child_weight=12,\n",
      "                                          missing=nan,\n",
      "                                          monotone_constraints=None,\n",
      "                                          multi_strategy=None, n_estimators=120,\n",
      "                                          n_jobs=1, num_parallel_tree=None, ...))])\n"
     ]
    }
   ],
   "source": [
    "# Inspect the candidate roster and example estimator configurations. The count\n",
    "# assertion guards the expected 17-model comparison; it does not evaluate accuracy.\n",
    "\n",
    "assert len(MODELS)==17\n",
    "print('Complete constrained features:',columns('constrained_level'))\n",
    "print(estimator('neural_network_change'))\n",
    "print(estimator('ensemble_change'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "226b2eca",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "Select models with the lowest development mean absolute error (MAE). Fit each PADD using earlier observations only.\n",
    "\n",
    "- **One month:** Use ten chronological tests of 12 eligible months. Select on U.S. error after summing PADD forecasts.\n",
    "- **Twelve months:** Compare all 17 methods over six annual forecast paths. Select separately on U.S. MAE. Predicted stocks feed subsequent months without actual data updates.\n",
    "\n",
    "Tests use revised EIA data and assume prior-month observations are available. July 2024–June 2026 is excluded from current model selection. Data from this period was already revised, so it is not the actual data that was present at the time. This means the model could perform better on new data than it actually would."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ca88dd62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:50.884897Z",
     "iopub.status.busy": "2026-09-16T22:03:50.884741Z",
     "iopub.status.idle": "2026-09-16T22:03:50.891944Z",
     "shell.execute_reply": "2026-09-16T22:03:50.891596Z"
    }
   },
   "outputs": [],
   "source": [
    "# Choose each PADD's one-step candidate on development data, evaluate the fixed\n",
    "# choices on the last 24 months, then refit winners for the live outlook.\n",
    "def evaluate(samples, folds=10, holdout=24, jobs=8):\n",
    "    \"\"\"Choose models only on development CV; score untouched final 24 months.\"\"\"\n",
    "    predictions, fold_metrics, selections, fitted, warning_rows = [], [], [], {}, []\n",
    "    best_parameters, searches = {}, []\n",
    "    for padd, frame in samples.items():\n",
    "        # Reserve the final holdout rows before any tuning or candidate selection.\n",
    "        development = frame.iloc[:-holdout]\n",
    "        if len(development) - folds*12 < 36:\n",
    "            raise ValueError('Need >=36 initial training months plus 10 annual folds and holdout')\n",
    "        splits = validation_splits(development, folds, test_size=12)\n",
    "        for name in MODELS:\n",
    "            params, results, warning = tune(name, development, folds, jobs, splits)\n",
    "            best_parameters[padd, name] = params\n",
    "            if not results.empty:\n",
    "                searches.append(results.assign(padd=padd, model=name, stage='development',\n",
    "                    train_end=development.month.max()))\n",
    "                print(f'PADD {padd} {name}: grid MAE {results.mean_test_mae_kb.min():,.1f}; {params}', flush=True)\n",
    "            if warning:\n",
    "                warning_rows.append(dict(padd=padd, model=name, stage='grid_search', warning=warning))\n",
    "            # Reuse tuning folds for development diagnostics; these are selection scores,\n",
    "            # not independent nested-CV estimates of future generalization.\n",
    "            for fold, (tr, te) in enumerate(splits, 1):\n",
    "                train, test = training_rows(development.iloc[tr]), development.iloc[te]\n",
    "                model, warning = fit(name, train, params)\n",
    "                pred = predict(name, model, test)\n",
    "                fold_metrics.append(dict(padd=padd, model=name, fold=fold,\n",
    "                    train_end=train.month.max(), test_start=test.month.min(), test_end=test.month.max(),\n",
    "                    train_mae_kb=mean_absolute_error(train.actual_kb, predict(name, model, train)),\n",
    "                    **score(test.actual_kb, pred)))\n",
    "                output = test[['month', 'origin_month', 'actual_kb']].copy()\n",
    "                output['predicted_kb'], output['padd'], output['model'] = pred, padd, name\n",
    "                output['split'], output['fold'] = 'cv', fold\n",
    "                predictions.append(output)\n",
    "                if warning:\n",
    "                    warning_rows.append(dict(padd=padd, model=name, stage=f'cv_{fold}', warning=warning))\n",
    "        current = pd.concat(predictions, ignore_index=True)\n",
    "        current = current[current.padd.eq(padd)]\n",
    "        # Pool absolute development errors for this PADD across folds and select\n",
    "        # the lowest mean; no holdout observations enter this comparison.\n",
    "        losses = current.assign(error=lambda x: abs(x.actual_kb-x.predicted_kb)).groupby('model').error.mean()\n",
    "        winner = losses.idxmin()\n",
    "        selections.append(dict(padd=padd, selected_model=winner, cv_mae_kb=losses[winner]))\n",
    "        for name in MODELS:\n",
    "            model, warning = fit(name, development, best_parameters[padd, name])\n",
    "            # This is one-step evaluation: each row uses actual prior-month inputs, while\n",
    "            # estimator parameters remain fitted on development data for the entire block.\n",
    "            test = frame.iloc[-holdout:]\n",
    "            output = test[['month', 'origin_month', 'actual_kb']].copy()\n",
    "            output['predicted_kb'] = predict(name, model, test)\n",
    "            output['padd'], output['model'], output['split'], output['fold'] = padd, name, 'holdout', 0\n",
    "            predictions.append(output)\n",
    "            if warning:\n",
    "                warning_rows.append(dict(padd=padd, model=name, stage='holdout', warning=warning))\n",
    "        # Refit the already chosen winner on all eligible history for future use.\n",
    "        # This final estimator is separate from the one that produced holdout scores.\n",
    "        final, warning = fit(winner, frame, best_parameters[padd, winner])\n",
    "        fitted[padd] = {'name': winner, 'estimator': final}\n",
    "        if warning:\n",
    "            warning_rows.append(dict(padd=padd, model=winner, stage='final', warning=warning))\n",
    "        print(f'PADD {padd}: selected {winner}; CV MAE {losses[winner]:,.1f} kb', flush=True)\n",
    "    return (pd.concat(predictions, ignore_index=True), pd.DataFrame(fold_metrics),\n",
    "            pd.DataFrame(selections), fitted,\n",
    "            pd.DataFrame(warning_rows, columns=['padd','model','stage','warning']),\n",
    "            best_parameters, pd.concat(searches, ignore_index=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a1b628bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:50.893434Z",
     "iopub.status.busy": "2026-09-16T22:03:50.893062Z",
     "iopub.status.idle": "2026-09-16T22:03:50.903566Z",
     "shell.execute_reply": "2026-09-16T22:03:50.903055Z"
    }
   },
   "outputs": [],
   "source": [
    "# Produce a recursive path per district plus national sums. After the origin,\n",
    "# features depend on predicted stocks/flows, not future actual observations.\n",
    "def forecast(history, fitted, horizon=12):\n",
    "    all_paths = []\n",
    "    for p, state in fitted.items():\n",
    "        past = history[history.padd.eq(p)].sort_values('month').copy()\n",
    "        origin = past.month.iloc[-1]\n",
    "        months = pd.date_range(origin + pd.offsets.MonthBegin(), periods=horizon, freq='MS')\n",
    "        # Forecast the full flow path once at the origin; do not refit flows each step\n",
    "        # using synthetic observations appended to the stock path.\n",
    "        flows = forecast_flows(past, months)\n",
    "        # Track a separate raw flow-identity path initialized at the last observed stock.\n",
    "        identity_stock = past.stock_kb.iloc[-1]\n",
    "        for h, month in enumerate(months, 1):\n",
    "            previous = past.stock_kb.iloc[-1]\n",
    "            flow = flows.loc[month]\n",
    "            balance = flow[FLOWS].to_numpy() @ SIGNS\n",
    "            # Crude balances are daily rates; multiply by target-month days to obtain\n",
    "            # the monthly stock increment in kb.\n",
    "            volume = balance * month.days_in_month\n",
    "            # At longer horizons, past contains earlier predicted rows. This is the recursive\n",
    "            # feedback through which forecast errors can propagate along the path.\n",
    "            features = feature_row(past, month)\n",
    "            features['forecast_flow_identity'] = max(0.0, previous + volume)\n",
    "            stock = float(predict(state['name'], state['estimator'], pd.DataFrame([features]))[0])\n",
    "            # Keep the raw flow accumulation unclipped so impossible inventory paths remain\n",
    "            # visible; it can diverge from the statistical stock forecast.\n",
    "            identity_stock += volume\n",
    "            row = dict(month=month, origin_month=origin, horizon=h, padd=p,\n",
    "                padd_name=PADD_NAMES[p], model=state['name'], stock_kb=stock,\n",
    "                previous_stock_kb=previous, stock_change_kb=stock-previous,\n",
    "                identity_stock_unclipped_kb=identity_stock, balance_kbd=balance,\n",
    "                # Express statistical stock change minus forecast net supply as a daily\n",
    "                # rate. This discrepancy is not an extra observed EIA adjustment.\n",
    "                model_reconciliation_kbd=(stock-previous)/month.days_in_month-balance,\n",
    "                supply_kbd=flow.production_kbd+flow.imports_kbd+flow.net_receipts_kbd+flow.adjustments_kbd+flow.transfers_kbd,\n",
    "                total_demand_kbd=flow.demand_kbd+flow.exports_kbd+flow.direct_use_kbd, **flow.to_dict())\n",
    "            all_paths.append(row)\n",
    "            # Append the predicted row so the next month uses this stock and these flows\n",
    "            # as its most recent history, without seeing future actual values.\n",
    "            past = pd.concat([past, pd.DataFrame([row])], ignore_index=True)\n",
    "    regional = pd.DataFrame(all_paths)\n",
    "    sums = FLOWS + ['stock_kb', 'previous_stock_kb','stock_change_kb', 'identity_stock_unclipped_kb',\n",
    "                   'balance_kbd','model_reconciliation_kbd','supply_kbd','total_demand_kbd','cdu_capacity_kbd']\n",
    "    national = aggregate(regional, ['month','origin_month','horizon'], sums)\n",
    "    # National utilization is total refinery input divided by total capacity,\n",
    "    # not a sum or unweighted average of regional utilization ratios.\n",
    "    national['utilization_ratio'] = national.demand_kbd / national.cdu_capacity_kbd\n",
    "    return regional, national"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "55f6b925",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:50.906799Z",
     "iopub.status.busy": "2026-09-16T22:03:50.906342Z",
     "iopub.status.idle": "2026-09-16T22:03:50.908845Z",
     "shell.execute_reply": "2026-09-16T22:03:50.908549Z"
    }
   },
   "outputs": [],
   "source": [
    "# Run controls: use ten expanding validation folds and eight search workers.\n",
    "# The worker count controls parallel grid trials; numerical threads are capped\n",
    "# inside fitting to avoid multiplying the number of active CPU threads.\n",
    "\n",
    "data_dir=HERE/'data'\n",
    "output_dir=OUTPUT\n",
    "folds=10\n",
    "jobs=8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fc516d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = Path(output_dir)\n",
    "# Build chronological supervised samples separately for all five districts,\n",
    "# then run the full development search, one-step holdout evaluation, and refits.\n",
    "# This is a computationally expensive cell; progress and warnings are retained.\n",
    "output.mkdir(parents=True, exist_ok=True)\n",
    "panel = load_panel(data_dir)\n",
    "histories = {p: panel[panel.padd.eq(p)].reset_index(drop=True) for p in PADD_NAMES}\n",
    "samples = {p: supervised(history) for p,history in histories.items()}\n",
    "predictions, fold_metrics, selection, fitted, fit_warnings, best_parameters, grid_results = evaluate(samples, folds, jobs=jobs)\n",
    "\n",
    "display(fold_metrics.groupby('model').agg(folds=('fold','count'),test_mae_kb=('mae_kb','mean')))\n",
    "display(selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd0b76b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For crude, compare the mixture of regional winners with each common model\n",
    "# family on national development MAE. Keep regional winners for audit, but deploy\n",
    "# the model with the smallest national error without using holdout scores.\n",
    "\n",
    "# Label the development-selected regional forecasts as a combined model while\n",
    "# retaining every original candidate's predictions for comparison.\n",
    "selected = predictions.merge(selection[['padd','selected_model']], on='padd')\n",
    "selected = selected[selected.model.eq(selected.selected_model)].copy()\n",
    "selected['model'] = 'selected_padd_models'\n",
    "combined = pd.concat([predictions, selected[predictions.columns]], ignore_index=True)\n",
    "us_predictions = aggregate(combined, ['month','origin_month','model','split','fold'], ['actual_kb','predicted_kb'])\n",
    "# Choose the aggregation model on development predictions only. A single\n",
    "# common model family can outperform the mix of regional winners nationally.\n",
    "one_month_model_scores = metric_table(us_predictions[us_predictions.split.eq('cv')], ['model'])\n",
    "# Select a common-family model or the mix of PADD winners by national CV MAE.\n",
    "# This extra selection is also based on development data and can be optimistic.\n",
    "one_month_model = one_month_model_scores.sort_values(['mae_kb','model']).iloc[0].model\n",
    "# Preserve regional-only winners before a possible national-model override.\n",
    "regional_selection = selection.copy()\n",
    "# When a common family wins nationally, refit that family separately for each\n",
    "# PADD and update deployment labels while retaining the original comparisons.\n",
    "if one_month_model != 'selected_padd_models':\n",
    "    for p, frame in samples.items():\n",
    "        fitted_estimator, warning = fit(one_month_model, frame, best_parameters[p, one_month_model])\n",
    "        fitted[p] = {'name': one_month_model, 'estimator': fitted_estimator}\n",
    "        if warning:\n",
    "            fit_warnings.loc[len(fit_warnings)] = [p, one_month_model, 'national_model_final', warning]\n",
    "    selection['selected_model'] = one_month_model\n",
    "    selection['cv_mae_kb'] = [mean_absolute_error(g.actual_kb, g.predicted_kb)\n",
    "        for p in selection.padd\n",
    "        for g in [predictions.query(\"padd == @p and model == @one_month_model and split == 'cv'\")]]\n",
    "padd_metrics = metric_table(combined, ['padd','model','split'])\n",
    "us_metrics = metric_table(us_predictions, ['model','split'])\n",
    "# Generate the next month using the selected one-step estimators. The separate\n",
    "# year-ahead selection below may choose a different family for the 12-month path.\n",
    "one_step_regional, one_step_national = forecast(panel, fitted, horizon=1)\n",
    "\n",
    "display(one_month_model_scores.sort_values('mae_kb'))\n",
    "print('One-month model:', one_month_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa8fd36e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate every candidate on six complete development-year forecast paths.\n",
    "# Retune at each historical origin, sum PADD predictions, then choose the common\n",
    "# year-ahead family using national MAE across all path months.\n",
    "\n",
    "# Long-horizon model selection is a different task. Six disjoint development\n",
    "# years end before the final holdout; all candidates use the actual recursive\n",
    "# forecast function, without seeing any test-year observations.\n",
    "development_end = samples[1].month.iloc[-25]\n",
    "recursive_cv = []\n",
    "for cutoff in recursive_origins(development_end):\n",
    "    for name in MODELS:\n",
    "        states = {}\n",
    "        for p in PADD_NAMES:\n",
    "            # Restrict fitting to targets observed by this simulated forecast origin.\n",
    "            train = samples[p][samples[p].month.le(cutoff)]\n",
    "            # Retune on this origin's history only; later development observations must not\n",
    "            # inform a model used to forecast an earlier evaluation year.\n",
    "            params, results, tuning_warning = tune(name, train, folds, jobs)\n",
    "            if not results.empty:\n",
    "                grid_results = pd.concat([grid_results, results.assign(padd=p, model=name,\n",
    "                    stage='recursive_development', train_end=cutoff)], ignore_index=True)\n",
    "            if tuning_warning:\n",
    "                fit_warnings.loc[len(fit_warnings)] = [p, name, 'recursive_grid_search', tuning_warning]\n",
    "            model, warning = fit(name, train, params)\n",
    "            states[p] = {'name': name, 'estimator': model}\n",
    "            if warning:\n",
    "                fit_warnings.loc[len(fit_warnings)] = [p, name, 'recursive_cv', warning]\n",
    "        path, _ = forecast(panel[panel.month.le(cutoff)], states)\n",
    "        path = path[['padd','month','origin_month','horizon','stock_kb']].rename(columns={'stock_kb':'predicted_kb'})\n",
    "        # Attach actual stocks only after forecasting, for scoring. one_to_one rejects\n",
    "        # duplicate month/PADD observations that could otherwise multiply rows.\n",
    "        path = path.merge(panel[['padd','month','stock_kb']], on=['padd','month'], validate='one_to_one').rename(columns={'stock_kb':'actual_kb'})\n",
    "        path['model'] = name\n",
    "        recursive_cv.append(path)\n",
    "    print(f'Recursive development origin {cutoff.date()} complete', flush=True)\n",
    "recursive_cv = pd.concat(recursive_cv, ignore_index=True)\n",
    "us_recursive_cv = aggregate(recursive_cv, ['month','origin_month','horizon','model'], ['actual_kb','predicted_kb'])\n",
    "year_ahead_model_scores = metric_table(us_recursive_cv, ['model']).sort_values(['mae_kb','model'])\n",
    "# Choose the lowest national development-path MAE, with model name as a stable\n",
    "# tie-breaker; freeze the family before examining recursive holdout results.\n",
    "year_ahead_model = year_ahead_model_scores.iloc[0].model\n",
    "# Refit the selected year-ahead family on full eligible history, using parameter\n",
    "# settings chosen before the holdout; then build the forward 12-month outlook.\n",
    "horizon_fitted = {}\n",
    "for p in PADD_NAMES:\n",
    "    model, warning = fit(year_ahead_model, samples[p], best_parameters[p, year_ahead_model])\n",
    "    horizon_fitted[p] = {'name': year_ahead_model, 'estimator': model}\n",
    "    if warning:\n",
    "        fit_warnings.loc[len(fit_warnings)] = [p, year_ahead_model, 'horizon_final', warning]\n",
    "regional, national = forecast(panel, horizon_fitted)\n",
    "print(f'National one-step model: {one_month_model}; 12-month model: {year_ahead_model}', flush=True)\n",
    "\n",
    "display(year_ahead_model_scores)\n",
    "print('Twelve-month model:', year_ahead_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8c990ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the fixed year-ahead model, recursive one-step model, and benchmarks\n",
    "# at 13 monthly origins. Refit on history available at each origin using frozen\n",
    "# development parameters; overlapping 12-month paths have correlated errors.\n",
    "\n",
    "# Thirteen rolling 12-month paths lying wholly inside the final holdout.\n",
    "# They overlap and are correlated; do not interpret them as 13 independent years.\n",
    "recursive = []\n",
    "# Move the origin forward one month at a time; each next 12 months remains\n",
    "# inside the final 24-month evaluation period.\n",
    "evaluation_models = list(dict.fromkeys([year_ahead_model, one_month_model, 'persistence', 'constrained_level']))\n",
    "for offset in range(24, 11, -1):\n",
    "    cutoff = panel.month.max() - pd.DateOffset(months=offset)\n",
    "    for name in evaluation_models:\n",
    "        states = {}\n",
    "        for p, state in fitted.items():\n",
    "            # Restrict fitting to targets observed by this simulated forecast origin.\n",
    "            train = samples[p][samples[p].month.le(cutoff)]\n",
    "            model, warning = fit(name, train, best_parameters[p, name])\n",
    "            states[p] = {'name': name, 'estimator': model}\n",
    "            if warning:\n",
    "                fit_warnings.loc[len(fit_warnings)] = [p, name, 'recursive_holdout', warning]\n",
    "        path, _ = forecast(panel[panel.month.le(cutoff)], states)\n",
    "        path['model'] = name\n",
    "        # Attach actual stocks only after forecasting, for scoring. one_to_one rejects\n",
    "        # duplicate month/PADD observations that could otherwise multiply rows.\n",
    "        path = path.merge(panel[['padd','month','stock_kb']], on=['padd','month'], suffixes=('_forecast','_actual'), validate='one_to_one')\n",
    "        path = path.rename(columns={'stock_kb_forecast':'predicted_kb','stock_kb_actual':'actual_kb'})\n",
    "        recursive.append(path)\n",
    "recursive = pd.concat(recursive, ignore_index=True)\n",
    "us_recursive = aggregate(recursive, ['month','origin_month','horizon','model'], ['actual_kb','predicted_kb'])\n",
    "\n",
    "display(metric_table(us_recursive, ['model']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccbdce99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine regional history into national totals and show the next-month\n",
    "# forecast. The latest table contains five PADD rows plus one U.S. reporting row.\n",
    "\n",
    "# Build national historical totals from all five districts. Ratios, when needed,\n",
    "# must be recomputed from their summed numerator and denominator.\n",
    "us_history = aggregate(panel, ['month'], FLOWS+['stock_kb','cdu_capacity_kbd',\n",
    "    'balance_kbd','accounting_residual_kbd','five_term_balance_kbd','five_term_residual_kbd'])\n",
    "us_history['utilization_ratio'] = us_history.demand_kbd / us_history.cdu_capacity_kbd\n",
    "# Use padd=0 as a reporting label for the U.S. total; it is not a sixth region.\n",
    "latest_us = one_step_national.assign(padd=0, padd_name='United States (sum of PADDs)',\n",
    "                                                   model=one_month_model)\n",
    "latest = pd.concat([one_step_regional, latest_us], ignore_index=True)\n",
    "\n",
    "display(us_history.tail(3))\n",
    "display(latest[['padd','padd_name','month','stock_kb']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49224dcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save result tables and fitted models for reporting and reuse. CSVs carry\n",
    "# readable diagnostics; joblib preserves estimator state; coefficients expose\n",
    "# the constrained regression's fitted relationship.\n",
    "\n",
    "# Collect inputs, predictions, scores, selections, and audits under stable names.\n",
    "# Each name becomes a CSV stem consumed by later reporting cells.\n",
    "tables = {'padd_monthly_model':panel,'us_monthly_model':us_history,\n",
    "    'padd_predictions':combined,'us_predictions':us_predictions,'padd_model_metrics':padd_metrics,\n",
    "    'us_model_metrics':us_metrics,'fold_metrics':fold_metrics,'selected_models':selection,\n",
    "    'regional_cv_winners':regional_selection, 'national_model_cv_metrics':one_month_model_scores,\n",
    "    'recursive_cv_predictions':recursive_cv, 'us_recursive_cv_predictions':us_recursive_cv,\n",
    "    'year_ahead_model_cv_metrics':year_ahead_model_scores,\n",
    "    'padd_forecast_12m':regional,'us_forecast_12m':national,'latest_forecast':latest,\n",
    "    'recursive_holdout_predictions':recursive,'us_recursive_holdout_predictions':us_recursive,\n",
    "    'recursive_holdout_metrics':metric_table(recursive,['padd','model']),\n",
    "    'us_recursive_horizon_metrics':metric_table(us_recursive,['horizon','model']), 'fit_warnings':fit_warnings,\n",
    "    'grid_search_results':grid_results,\n",
    "    'best_parameters':pd.DataFrame([{'padd':p, 'model':name, 'params':json.dumps(params, sort_keys=True)}\n",
    "        for (p,name),params in best_parameters.items()])}\n",
    "# Record target and origin dates plus separate fitting/evaluation eligibility\n",
    "# flags so readers can inspect exactly which dates the exclusion model affects.\n",
    "tables['training_sample_audit'] = pd.concat([frame[['month','origin_month']].assign(\n",
    "    padd=p, training_eligible=frame.index.isin(training_rows(frame).index),\n",
    "    evaluation_eligible=outside_covid(frame.month)) for p, frame in samples.items()], ignore_index=True)\n",
    "# Write named result tables without pandas' row index; rerunning replaces files\n",
    "# with the same names in model_output.\n",
    "for name, frame in tables.items():\n",
    "    frame.to_csv(output/f'{name}.csv', index=False)\n",
    "# Save all final candidate estimators as well as selected models so downstream\n",
    "# analysis can reproduce predictions without repeating parameter searches.\n",
    "all_fitted = {}\n",
    "for p, frame in samples.items():\n",
    "    for name in MODELS:\n",
    "        model, warning = fit(name, frame, best_parameters[p, name])\n",
    "        all_fitted[p, name] = {'name': name, 'estimator': model}\n",
    "joblib.dump(all_fitted, output/'all_fitted_models.joblib')\n",
    "joblib.dump(fitted, output/'fitted_models.joblib')\n",
    "joblib.dump(horizon_fitted, output/'fitted_horizon_models.joblib')\n",
    "# Export an interpretable constrained-regression coefficient table by PADD.\n",
    "# These coefficients apply to pre-signed inputs, not unsigned raw demand flows.\n",
    "coefficients = []\n",
    "for p, frame in samples.items():\n",
    "    model, _ = fit('constrained_level', frame)\n",
    "    coefficients.append({'padd':p,'intercept_kb':model.intercept_,**dict(zip(columns('constrained_level'),model.coef_))})\n",
    "pd.DataFrame(coefficients).to_csv(output/'constrained_model_coefficients.csv',index=False)\n",
    "pd.DataFrame(coefficients).to_csv(output/'padd_model_coefficients.csv',index=False)\n",
    "\n",
    "print('Saved',len(tables),'result tables to',output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbb6ec3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Persist machine-readable run documentation alongside result tables. Source\n",
    "# URLs identify provenance; versions and settings describe the actual run,\n",
    "# including limitations of revised-history and point-only forecasts.\n",
    "\n",
    "# Record scope, units, actual data dates, selection rules, limitations, and\n",
    "# software versions alongside outputs for reproducibility and interpretation.\n",
    "metadata = {'product':'Commercial crude oil; excludes strategic reserves',\n",
    "    'covid_exclusion': {'start':'2020-03-01', 'end':'2021-03-01', 'inclusive':True,\n",
    "        'method':'Exclude targets and direct lag inputs touching COVID from stock fitting; exclude COVID observations from flow fits and seasonal averages; preserve the full calendar and historical balance.',\n",
    "        'evaluation':'Exclude COVID target months from development selection; full recursive development years avoid COVID; final 24 months unchanged.'},\n",
    "    'stock_units':'thousand barrels at month end', 'flow_units':'thousand barrels per day',\n",
    "    'equation':'S[t] = S[t-1] + days[t] * (field production[t] + imports[t] + net receipts[t] + adjustments[t] + transfers to crude[t] - refinery input[t] - exports[t] - direct use[t] + residual[t])',\n",
    "    'data_start':str(panel.month.min().date()),'data_end':str(panel.month.max().date()),\n",
    "    'cv':f'{folds} expanding folds, 12 eligible test months each; final 24 months excluded from selection',\n",
    "    'holdout_start':str(samples[1].month.iloc[-24].date()),\n",
    "    'forecast_timing':'One month after latest observed EIA month; conditional on prior monthly data being available. Current revised history, not a real-time release-vintage backtest.',\n",
    "    'selection':'GridSearchCV per PADD/model on development data; PADD winners plus common-family models; lowest national development MAE chooses deployment; no holdout tuning',\n",
    "    'hyperparameter_search':{'method':'GridSearchCV', 'folds':folds, 'scoring':'neg_mean_absolute_error',\n",
    "        'splitter':'TimeSeriesSplit on non-COVID target dates; 12 eligible observations per development block; default block size inside recursive origins',\n",
    "        'evaluation':'Development scores reuse tuning folds and are selection scores, not nested CV estimates. Final 24 months excluded from all searches.',\n",
    "        'recursive':'Each development origin retunes on its past history. Holdout and final refits freeze parameters selected before holdout.'},\n",
    "    'one_month_model':one_month_model,\n",
    "    'year_ahead_model':year_ahead_model,\n",
    "    'horizon_selection':'Lowest national MAE across six disjoint 12-month development paths, six full non-COVID development years; all 17 candidates compared',\n",
    "    'flow_forecast':'Last 60 non-COVID observations, daily rates, linear trend and month fixed effects; crude run = forecast utilization ratio (clipped 0–1) times CDU capacity fixed at origin',\n",
    "    'forecast_stock_floor':0, 'uncertainty':'Point forecasts only; no calibrated prediction intervals',\n",
    "    'recursive_validation':'13 overlapping 12-month paths entirely in final holdout; refit at each origin; correlated errors',\n",
    "    'versions':{'python':platform.python_version(),'numpy':np.__version__,'pandas':pd.__version__,\n",
    "                'sklearn':sklearn.__version__, 'xgboost':xgboost.__version__},\n",
    "    'sources':['https://www.eia.gov/dnav/pet/pet_sum_snd_d_r10_mbbl_m_cur.htm',\n",
    "               'https://www.eia.gov/dnav/pet/TblDefs/pet_sum_snd_tbldef2.asp']}\n",
    "(output/'model_metadata.json').write_text(json.dumps(metadata,indent=2))\n",
    "# Serialize candidate definitions for inspection; repr describes configuration,\n",
    "# while the joblib files above preserve actual fitted estimator state.\n",
    "settings = {name: {'features': columns(name), 'estimator': repr(estimator(name)), 'param_grid':parameter_grid(name)}\n",
    "            for name in LEARNED}\n",
    "(output/'candidate_models.json').write_text(json.dumps(settings, indent=2))\n",
    "print(us_metrics[us_metrics.split.eq('holdout')].sort_values('mae_kb').to_string(index=False))\n",
    "\n",
    "print('Data through:',metadata['data_end'])\n",
    "print('Fitting warnings:',len(fit_warnings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd88f23f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:51.035272Z",
     "iopub.status.busy": "2026-09-16T22:03:51.035132Z",
     "iopub.status.idle": "2026-09-16T22:03:51.044587Z",
     "shell.execute_reply": "2026-09-16T22:03:51.043937Z"
    }
   },
   "outputs": [
    {
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       "      <th>test_start</th>\n",
       "      <th>test_end</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>2014-05-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2014-05-01</td>\n",
       "      <td>2014-06-01</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fold  train_end test_start   test_end   n\n",
       "0     1 2013-05-01 2013-06-01 2014-05-01  12\n",
       "1     2 2014-05-01 2014-06-01 2015-05-01  12\n",
       "2     3 2015-05-01 2015-06-01 2016-05-01  12\n",
       "3     4 2016-05-01 2016-06-01 2017-05-01  12\n",
       "4     5 2017-05-01 2017-06-01 2018-05-01  12\n",
       "5     6 2018-05-01 2018-06-01 2019-05-01  12\n",
       "6     7 2019-05-01 2019-06-01 2021-06-01  12\n",
       "7     8 2020-02-01 2021-07-01 2022-06-01  12\n",
       "8     9 2022-06-01 2022-07-01 2023-06-01  12\n",
       "9    10 2023-06-01 2023-07-01 2024-06-01  12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "folds=tables['fold_metrics']\n",
    "# Inspect one representative model/PADD because candidates share split dates.\n",
    "# Here folds is reassigned from the earlier integer setting to the metrics table.\n",
    "# Assertions require training before testing and development before the holdout.\n",
    "schedule=folds.query(\"padd==1 and model=='persistence'\")[['fold','train_end','test_start','test_end','n']]\n",
    "display(schedule)\n",
    "assert (pd.to_datetime(schedule.train_end)<pd.to_datetime(schedule.test_start)).all()\n",
    "assert pd.to_datetime(schedule.test_end).max()<pd.Timestamp(metadata['holdout_start'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "030710ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:51.045897Z",
     "iopub.status.busy": "2026-09-16T22:03:51.045745Z",
     "iopub.status.idle": "2026-09-16T22:03:51.191799Z",
     "shell.execute_reply": "2026-09-16T22:03:51.191141Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>padd</th>\n",
       "      <th>selected_model</th>\n",
       "      <th>cv_mae_kb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>1,046.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>damped_ridge_change</td>\n",
       "      <td>4,258.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>6,222.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>738.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>spline_ridge_change</td>\n",
       "      <td>1,646.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   padd            selected_model  cv_mae_kb\n",
       "0     1     neural_network_change   1,046.18\n",
       "1     2       damped_ridge_change   4,258.24\n",
       "2     3           seasonal_change   6,222.72\n",
       "3     4  seasonal_residual_change     738.58\n",
       "4     5       spline_ridge_change   1,646.08"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mae_kb</th>\n",
       "      <th>rmse_kb</th>\n",
       "      <th>r2</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>8,190.01</td>\n",
       "      <td>10,450.37</td>\n",
       "      <td>0.95</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>8,232.03</td>\n",
       "      <td>10,340.39</td>\n",
       "      <td>0.95</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>selected_padd_models</td>\n",
       "      <td>8,440.53</td>\n",
       "      <td>10,453.34</td>\n",
       "      <td>0.95</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>9,051.74</td>\n",
       "      <td>11,385.25</td>\n",
       "      <td>0.94</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>9,069.75</td>\n",
       "      <td>11,319.01</td>\n",
       "      <td>0.94</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>spline_ridge_change</td>\n",
       "      <td>9,318.61</td>\n",
       "      <td>11,109.33</td>\n",
       "      <td>0.95</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ensemble_change</td>\n",
       "      <td>9,384.75</td>\n",
       "      <td>11,592.29</td>\n",
       "      <td>0.94</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>polynomial_ridge_change</td>\n",
       "      <td>9,903.90</td>\n",
       "      <td>12,047.77</td>\n",
       "      <td>0.94</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>huber_change</td>\n",
       "      <td>10,014.32</td>\n",
       "      <td>12,339.43</td>\n",
       "      <td>0.93</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>10,044.17</td>\n",
       "      <td>12,263.98</td>\n",
       "      <td>0.93</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>rolling_ridge_change</td>\n",
       "      <td>10,098.67</td>\n",
       "      <td>12,533.07</td>\n",
       "      <td>0.93</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>damped_ridge_change</td>\n",
       "      <td>10,152.76</td>\n",
       "      <td>12,435.16</td>\n",
       "      <td>0.93</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>seasonal_ridge_change</td>\n",
       "      <td>10,162.22</td>\n",
       "      <td>12,429.43</td>\n",
       "      <td>0.93</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ridge_change</td>\n",
       "      <td>10,564.22</td>\n",
       "      <td>12,964.16</td>\n",
       "      <td>0.93</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>persistence</td>\n",
       "      <td>10,818.33</td>\n",
       "      <td>13,133.21</td>\n",
       "      <td>0.92</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>constrained_level</td>\n",
       "      <td>11,103.92</td>\n",
       "      <td>14,235.30</td>\n",
       "      <td>0.91</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>forecast_flow_identity</td>\n",
       "      <td>11,266.31</td>\n",
       "      <td>14,790.30</td>\n",
       "      <td>0.90</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>39,776.22</td>\n",
       "      <td>50,836.94</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       model    mae_kb   rmse_kb    r2    n\n",
       "11           seasonal_change  8,190.01 10,450.37  0.95  120\n",
       "13  seasonal_residual_change  8,232.03 10,340.39  0.95  120\n",
       "15      selected_padd_models  8,440.53 10,453.34  0.95  120\n",
       "17            xgboost_change  9,051.74 11,385.25  0.94  120\n",
       "8       random_forest_change  9,069.75 11,319.01  0.94  120\n",
       "16       spline_ridge_change  9,318.61 11,109.33  0.95  120\n",
       "2            ensemble_change  9,384.75 11,592.29  0.94  120\n",
       "7    polynomial_ridge_change  9,903.90 12,047.77  0.94  120\n",
       "4               huber_change 10,014.32 12,339.43  0.93  120\n",
       "5      neural_network_change 10,044.17 12,263.98  0.93  120\n",
       "10      rolling_ridge_change 10,098.67 12,533.07  0.93  120\n",
       "1        damped_ridge_change 10,152.76 12,435.16  0.93  120\n",
       "14     seasonal_ridge_change 10,162.22 12,429.43  0.93  120\n",
       "9               ridge_change 10,564.22 12,964.16  0.93  120\n",
       "6                persistence 10,818.33 13,133.21  0.92  120\n",
       "0          constrained_level 11,103.92 14,235.30  0.91  120\n",
       "3     forecast_flow_identity 11,266.31 14,790.30  0.90  120\n",
       "12            seasonal_naive 39,776.22 50,836.94 -0.15  120"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_3a392_row1_col1, #T_3a392_row5_col0, #T_3a392_row11_col2, #T_3a392_row13_col3, #T_3a392_row15_col4 {\n",
       "  background-color: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_3a392\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >padd</th>\n",
       "      <th id=\"T_3a392_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_3a392_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_3a392_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "      <th id=\"T_3a392_level0_col3\" class=\"col_heading level0 col3\" >4</th>\n",
       "      <th id=\"T_3a392_level0_col4\" class=\"col_heading level0 col4\" >5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >model</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row0\" class=\"row_heading level0 row0\" >constrained_level</th>\n",
       "      <td id=\"T_3a392_row0_col0\" class=\"data row0 col0\" >1240.045865</td>\n",
       "      <td id=\"T_3a392_row0_col1\" class=\"data row0 col1\" >4651.975993</td>\n",
       "      <td id=\"T_3a392_row0_col2\" class=\"data row0 col2\" >8145.071871</td>\n",
       "      <td id=\"T_3a392_row0_col3\" class=\"data row0 col3\" >887.980370</td>\n",
       "      <td id=\"T_3a392_row0_col4\" class=\"data row0 col4\" >1720.386926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row1\" class=\"row_heading level0 row1\" >damped_ridge_change</th>\n",
       "      <td id=\"T_3a392_row1_col0\" class=\"data row1 col0\" >1051.416827</td>\n",
       "      <td id=\"T_3a392_row1_col1\" class=\"data row1 col1\" >4258.244729</td>\n",
       "      <td id=\"T_3a392_row1_col2\" class=\"data row1 col2\" >7674.048251</td>\n",
       "      <td id=\"T_3a392_row1_col3\" class=\"data row1 col3\" >785.185145</td>\n",
       "      <td id=\"T_3a392_row1_col4\" class=\"data row1 col4\" >1728.138425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row2\" class=\"row_heading level0 row2\" >ensemble_change</th>\n",
       "      <td id=\"T_3a392_row2_col0\" class=\"data row2 col0\" >1061.771003</td>\n",
       "      <td id=\"T_3a392_row2_col1\" class=\"data row2 col1\" >4298.513596</td>\n",
       "      <td id=\"T_3a392_row2_col2\" class=\"data row2 col2\" >7011.194897</td>\n",
       "      <td id=\"T_3a392_row2_col3\" class=\"data row2 col3\" >773.741179</td>\n",
       "      <td id=\"T_3a392_row2_col4\" class=\"data row2 col4\" >1756.959525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row3\" class=\"row_heading level0 row3\" >forecast_flow_identity</th>\n",
       "      <td id=\"T_3a392_row3_col0\" class=\"data row3 col0\" >2306.896990</td>\n",
       "      <td id=\"T_3a392_row3_col1\" class=\"data row3 col1\" >5010.608114</td>\n",
       "      <td id=\"T_3a392_row3_col2\" class=\"data row3 col2\" >9005.547167</td>\n",
       "      <td id=\"T_3a392_row3_col3\" class=\"data row3 col3\" >811.827747</td>\n",
       "      <td id=\"T_3a392_row3_col4\" class=\"data row3 col4\" >2013.300947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row4\" class=\"row_heading level0 row4\" >huber_change</th>\n",
       "      <td id=\"T_3a392_row4_col0\" class=\"data row4 col0\" >1077.731035</td>\n",
       "      <td id=\"T_3a392_row4_col1\" class=\"data row4 col1\" >4309.789931</td>\n",
       "      <td id=\"T_3a392_row4_col2\" class=\"data row4 col2\" >7591.271419</td>\n",
       "      <td id=\"T_3a392_row4_col3\" class=\"data row4 col3\" >925.121139</td>\n",
       "      <td id=\"T_3a392_row4_col4\" class=\"data row4 col4\" >1758.239635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row5\" class=\"row_heading level0 row5\" >neural_network_change</th>\n",
       "      <td id=\"T_3a392_row5_col0\" class=\"data row5 col0\" >1046.178623</td>\n",
       "      <td id=\"T_3a392_row5_col1\" class=\"data row5 col1\" >4304.095395</td>\n",
       "      <td id=\"T_3a392_row5_col2\" class=\"data row5 col2\" >7646.017868</td>\n",
       "      <td id=\"T_3a392_row5_col3\" class=\"data row5 col3\" >811.739484</td>\n",
       "      <td id=\"T_3a392_row5_col4\" class=\"data row5 col4\" >1777.984012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row6\" class=\"row_heading level0 row6\" >persistence</th>\n",
       "      <td id=\"T_3a392_row6_col0\" class=\"data row6 col0\" >1114.841667</td>\n",
       "      <td id=\"T_3a392_row6_col1\" class=\"data row6 col1\" >4572.183333</td>\n",
       "      <td id=\"T_3a392_row6_col2\" class=\"data row6 col2\" >7835.491667</td>\n",
       "      <td id=\"T_3a392_row6_col3\" class=\"data row6 col3\" >811.408333</td>\n",
       "      <td id=\"T_3a392_row6_col4\" class=\"data row6 col4\" >1886.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row7\" class=\"row_heading level0 row7\" >polynomial_ridge_change</th>\n",
       "      <td id=\"T_3a392_row7_col0\" class=\"data row7 col0\" >1060.653663</td>\n",
       "      <td id=\"T_3a392_row7_col1\" class=\"data row7 col1\" >4260.349419</td>\n",
       "      <td id=\"T_3a392_row7_col2\" class=\"data row7 col2\" >7551.761366</td>\n",
       "      <td id=\"T_3a392_row7_col3\" class=\"data row7 col3\" >809.733439</td>\n",
       "      <td id=\"T_3a392_row7_col4\" class=\"data row7 col4\" >1773.842029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row8\" class=\"row_heading level0 row8\" >random_forest_change</th>\n",
       "      <td id=\"T_3a392_row8_col0\" class=\"data row8 col0\" >1074.623520</td>\n",
       "      <td id=\"T_3a392_row8_col1\" class=\"data row8 col1\" >4401.772969</td>\n",
       "      <td id=\"T_3a392_row8_col2\" class=\"data row8 col2\" >6683.817144</td>\n",
       "      <td id=\"T_3a392_row8_col3\" class=\"data row8 col3\" >744.434036</td>\n",
       "      <td id=\"T_3a392_row8_col4\" class=\"data row8 col4\" >1763.691864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row9\" class=\"row_heading level0 row9\" >ridge_change</th>\n",
       "      <td id=\"T_3a392_row9_col0\" class=\"data row9 col0\" >1074.791505</td>\n",
       "      <td id=\"T_3a392_row9_col1\" class=\"data row9 col1\" >4374.943200</td>\n",
       "      <td id=\"T_3a392_row9_col2\" class=\"data row9 col2\" >7845.659070</td>\n",
       "      <td id=\"T_3a392_row9_col3\" class=\"data row9 col3\" >816.848524</td>\n",
       "      <td id=\"T_3a392_row9_col4\" class=\"data row9 col4\" >1754.866718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row10\" class=\"row_heading level0 row10\" >rolling_ridge_change</th>\n",
       "      <td id=\"T_3a392_row10_col0\" class=\"data row10 col0\" >1114.413595</td>\n",
       "      <td id=\"T_3a392_row10_col1\" class=\"data row10 col1\" >4378.170011</td>\n",
       "      <td id=\"T_3a392_row10_col2\" class=\"data row10 col2\" >7418.082881</td>\n",
       "      <td id=\"T_3a392_row10_col3\" class=\"data row10 col3\" >805.171253</td>\n",
       "      <td id=\"T_3a392_row10_col4\" class=\"data row10 col4\" >1779.949768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row11\" class=\"row_heading level0 row11\" >seasonal_change</th>\n",
       "      <td id=\"T_3a392_row11_col0\" class=\"data row11 col0\" >1232.179167</td>\n",
       "      <td id=\"T_3a392_row11_col1\" class=\"data row11 col1\" >4690.117083</td>\n",
       "      <td id=\"T_3a392_row11_col2\" class=\"data row11 col2\" >6222.722083</td>\n",
       "      <td id=\"T_3a392_row11_col3\" class=\"data row11 col3\" >757.695000</td>\n",
       "      <td id=\"T_3a392_row11_col4\" class=\"data row11 col4\" >1809.377917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row12\" class=\"row_heading level0 row12\" >seasonal_naive</th>\n",
       "      <td id=\"T_3a392_row12_col0\" class=\"data row12 col0\" >2099.583333</td>\n",
       "      <td id=\"T_3a392_row12_col1\" class=\"data row12 col1\" >17976.983333</td>\n",
       "      <td id=\"T_3a392_row12_col2\" class=\"data row12 col2\" >23205.116667</td>\n",
       "      <td id=\"T_3a392_row12_col3\" class=\"data row12 col3\" >1372.358333</td>\n",
       "      <td id=\"T_3a392_row12_col4\" class=\"data row12 col4\" >2907.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row13\" class=\"row_heading level0 row13\" >seasonal_residual_change</th>\n",
       "      <td id=\"T_3a392_row13_col0\" class=\"data row13 col0\" >1113.166513</td>\n",
       "      <td id=\"T_3a392_row13_col1\" class=\"data row13 col1\" >4399.248253</td>\n",
       "      <td id=\"T_3a392_row13_col2\" class=\"data row13 col2\" >6251.620832</td>\n",
       "      <td id=\"T_3a392_row13_col3\" class=\"data row13 col3\" >738.580768</td>\n",
       "      <td id=\"T_3a392_row13_col4\" class=\"data row13 col4\" >1659.496021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row14\" class=\"row_heading level0 row14\" >seasonal_ridge_change</th>\n",
       "      <td id=\"T_3a392_row14_col0\" class=\"data row14 col0\" >1060.653663</td>\n",
       "      <td id=\"T_3a392_row14_col1\" class=\"data row14 col1\" >4260.349419</td>\n",
       "      <td id=\"T_3a392_row14_col2\" class=\"data row14 col2\" >7599.706932</td>\n",
       "      <td id=\"T_3a392_row14_col3\" class=\"data row14 col3\" >812.964942</td>\n",
       "      <td id=\"T_3a392_row14_col4\" class=\"data row14 col4\" >1773.842029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row15\" class=\"row_heading level0 row15\" >spline_ridge_change</th>\n",
       "      <td id=\"T_3a392_row15_col0\" class=\"data row15 col0\" >1088.705381</td>\n",
       "      <td id=\"T_3a392_row15_col1\" class=\"data row15 col1\" >4437.362796</td>\n",
       "      <td id=\"T_3a392_row15_col2\" class=\"data row15 col2\" >7222.400995</td>\n",
       "      <td id=\"T_3a392_row15_col3\" class=\"data row15 col3\" >797.185309</td>\n",
       "      <td id=\"T_3a392_row15_col4\" class=\"data row15 col4\" >1646.076458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a392_level0_row16\" class=\"row_heading level0 row16\" >xgboost_change</th>\n",
       "      <td id=\"T_3a392_row16_col0\" class=\"data row16 col0\" >1073.722131</td>\n",
       "      <td id=\"T_3a392_row16_col1\" class=\"data row16 col1\" >4359.164779</td>\n",
       "      <td id=\"T_3a392_row16_col2\" class=\"data row16 col2\" >6568.209115</td>\n",
       "      <td id=\"T_3a392_row16_col3\" class=\"data row16 col3\" >743.130029</td>\n",
       "      <td id=\"T_3a392_row16_col4\" class=\"data row16 col4\" >1732.058887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7401bc780d70>"
      ]
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       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train_mae_kb</th>\n",
       "      <th>mae_kb</th>\n",
       "      <th>test_minus_train_kb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>seasonal_residual_change</th>\n",
       "      <td>2,365.32</td>\n",
       "      <td>2,832.42</td>\n",
       "      <td>467.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost_change</th>\n",
       "      <td>1,468.34</td>\n",
       "      <td>2,895.26</td>\n",
       "      <td>1,426.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_forest_change</th>\n",
       "      <td>1,790.16</td>\n",
       "      <td>2,933.67</td>\n",
       "      <td>1,143.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_change</th>\n",
       "      <td>2,635.56</td>\n",
       "      <td>2,942.42</td>\n",
       "      <td>306.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble_change</th>\n",
       "      <td>2,184.63</td>\n",
       "      <td>2,980.44</td>\n",
       "      <td>795.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spline_ridge_change</th>\n",
       "      <td>2,251.17</td>\n",
       "      <td>3,038.35</td>\n",
       "      <td>787.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>polynomial_ridge_change</th>\n",
       "      <td>2,457.46</td>\n",
       "      <td>3,091.27</td>\n",
       "      <td>633.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rolling_ridge_change</th>\n",
       "      <td>2,848.19</td>\n",
       "      <td>3,099.16</td>\n",
       "      <td>250.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>damped_ridge_change</th>\n",
       "      <td>2,529.27</td>\n",
       "      <td>3,099.41</td>\n",
       "      <td>570.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_ridge_change</th>\n",
       "      <td>2,477.46</td>\n",
       "      <td>3,101.50</td>\n",
       "      <td>624.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neural_network_change</th>\n",
       "      <td>2,211.56</td>\n",
       "      <td>3,117.20</td>\n",
       "      <td>905.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huber_change</th>\n",
       "      <td>2,306.29</td>\n",
       "      <td>3,132.43</td>\n",
       "      <td>826.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge_change</th>\n",
       "      <td>2,534.75</td>\n",
       "      <td>3,173.42</td>\n",
       "      <td>638.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>persistence</th>\n",
       "      <td>2,773.12</td>\n",
       "      <td>3,244.18</td>\n",
       "      <td>471.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>constrained_level</th>\n",
       "      <td>2,451.07</td>\n",
       "      <td>3,329.09</td>\n",
       "      <td>878.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_flow_identity</th>\n",
       "      <td>3,211.49</td>\n",
       "      <td>3,829.64</td>\n",
       "      <td>618.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_naive</th>\n",
       "      <td>7,338.88</td>\n",
       "      <td>9,512.22</td>\n",
       "      <td>2,173.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          train_mae_kb   mae_kb  test_minus_train_kb\n",
       "model                                                               \n",
       "seasonal_residual_change      2,365.32 2,832.42               467.11\n",
       "xgboost_change                1,468.34 2,895.26             1,426.92\n",
       "random_forest_change          1,790.16 2,933.67             1,143.51\n",
       "seasonal_change               2,635.56 2,942.42               306.86\n",
       "ensemble_change               2,184.63 2,980.44               795.80\n",
       "spline_ridge_change           2,251.17 3,038.35               787.18\n",
       "polynomial_ridge_change       2,457.46 3,091.27               633.80\n",
       "rolling_ridge_change          2,848.19 3,099.16               250.97\n",
       "damped_ridge_change           2,529.27 3,099.41               570.14\n",
       "seasonal_ridge_change         2,477.46 3,101.50               624.04\n",
       "neural_network_change         2,211.56 3,117.20               905.64\n",
       "huber_change                  2,306.29 3,132.43               826.14\n",
       "ridge_change                  2,534.75 3,173.42               638.67\n",
       "persistence                   2,773.12 3,244.18               471.07\n",
       "constrained_level             2,451.07 3,329.09               878.02\n",
       "forecast_flow_identity        3,211.49 3,829.64               618.14\n",
       "seasonal_naive                7,338.88 9,512.22             2,173.35"
      ]
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare development MAE by candidate and district, then inspect the average\n",
    "# train/test gap. A large gap suggests overfitting or a changing data regime;\n",
    "# these reused selection scores are not an independent performance estimate.\n",
    "\n",
    "display(tables['regional_cv_winners'])\n",
    "display(tables['national_model_cv_metrics'].sort_values('mae_kb'))\n",
    "cv=tables['padd_model_metrics'].query(\"split == 'cv' and model != 'selected_padd_models'\")\n",
    "display(cv.pivot(index='model',columns='padd',values='mae_kb').style.highlight_min(axis=0))\n",
    "gaps=folds.groupby('model')[['train_mae_kb','mae_kb']].mean().sort_values('mae_kb')\n",
    "# Subtract in-sample MAE from validation MAE; positive gaps mean worse\n",
    "# performance on later observations than on fitted training observations.\n",
    "gaps['test_minus_train_kb']=gaps.mae_kb-gaps.train_mae_kb\n",
    "display(gaps)\n",
    "gaps[['train_mae_kb','mae_kb']].plot.barh(figsize=(10,6),title='Mean training and annual-test errors across PADDs')\n",
    "plt.xlabel('Thousand barrels');plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4edcbd34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:51.193198Z",
     "iopub.status.busy": "2026-09-16T22:03:51.192961Z",
     "iopub.status.idle": "2026-09-16T22:03:51.284671Z",
     "shell.execute_reply": "2026-09-16T22:03:51.284080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>padd</th>\n",
       "      <th>intercept_kb</th>\n",
       "      <th>stock_lag1</th>\n",
       "      <th>lag_production_kbd</th>\n",
       "      <th>lag_demand_kbd</th>\n",
       "      <th>lag_imports_kbd</th>\n",
       "      <th>lag_exports_kbd</th>\n",
       "      <th>lag_net_receipts_kbd</th>\n",
       "      <th>lag_adjustments_kbd</th>\n",
       "      <th>lag_transfers_kbd</th>\n",
       "      <th>lag_direct_use_kbd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1,600.17</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.85</td>\n",
       "      <td>4.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>13,336.58</td>\n",
       "      <td>0.92</td>\n",
       "      <td>13.08</td>\n",
       "      <td>12.13</td>\n",
       "      <td>9.12</td>\n",
       "      <td>6.41</td>\n",
       "      <td>10.35</td>\n",
       "      <td>6.09</td>\n",
       "      <td>10.98</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>35,906.85</td>\n",
       "      <td>0.90</td>\n",
       "      <td>4.68</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0.26</td>\n",
       "      <td>5.11</td>\n",
       "      <td>1.60</td>\n",
       "      <td>5.88</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>-439.18</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.10</td>\n",
       "      <td>5.23</td>\n",
       "      <td>2.57</td>\n",
       "      <td>3.77</td>\n",
       "      <td>5.98</td>\n",
       "      <td>11.97</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>22,636.18</td>\n",
       "      <td>0.48</td>\n",
       "      <td>13.95</td>\n",
       "      <td>8.25</td>\n",
       "      <td>6.56</td>\n",
       "      <td>16.32</td>\n",
       "      <td>10.99</td>\n",
       "      <td>8.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   padd  intercept_kb  stock_lag1  lag_production_kbd  lag_demand_kbd  \\\n",
       "0     1      1,600.17        0.78                0.00            0.00   \n",
       "1     2     13,336.58        0.92               13.08           12.13   \n",
       "2     3     35,906.85        0.90                4.68            4.70   \n",
       "3     4       -439.18        0.87                5.23            1.10   \n",
       "4     5     22,636.18        0.48               13.95            8.25   \n",
       "\n",
       "   lag_imports_kbd  lag_exports_kbd  lag_net_receipts_kbd  \\\n",
       "0             0.81             0.00                  1.85   \n",
       "1             9.12             6.41                 10.35   \n",
       "2             0.26             5.11                  1.60   \n",
       "3             5.23             2.57                  3.77   \n",
       "4             6.56            16.32                 10.99   \n",
       "\n",
       "   lag_adjustments_kbd  lag_transfers_kbd  lag_direct_use_kbd  \n",
       "0                 4.21               0.00                0.00  \n",
       "1                 6.09              10.98                0.00  \n",
       "2                 5.88               2.25                0.00  \n",
       "3                 5.98              11.97                0.00  \n",
       "4                 8.38               0.00                0.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group validation errors by test-block endpoint to show regime variation.\n",
    "# These plotted values average regional MAEs, not errors on national stock sums.\n",
    "# The coefficient CSV provides a separate view of the constrained model.\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(12,4))\n",
    "for name in ['persistence','seasonal_change','constrained_level','xgboost_change','rolling_ridge_change']:\n",
    "    g=folds[folds.model.eq(name)].groupby('test_end').mae_kb.mean()\n",
    "    ax.plot(pd.to_datetime(g.index),g.values,label=name,marker='o')\n",
    "ax.set(title='Annual test performance changes across regimes',ylabel='Mean PADD MAE (kb)')\n",
    "ax.legend(fontsize=8);plt.show()\n",
    "display(pd.read_csv(OUTPUT/'constrained_model_coefficients.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c86db6",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "Compare the model’s forecasts with two simple forecasts over July 2024–June 2026:\n",
    "\n",
    "- **Unchanged stocks:** assume next month’s inventory equals the latest observed inventory.\n",
    "- **Seasonal benchmark:** assume inventories follow their typical pattern for that month of the year.\n",
    "\n",
    "The model improves on a benchmark if its forecast errors are smaller.\n",
    "Mean absolute error (MAE) measures the average forecast miss; root mean squared error (RMSE) gives more weight to large misses. Errors are in thousand barrels unless labeled otherwise. Lower is better. R² measures fit to stock levels, not accuracy in predicting builds and draws."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a26f0859",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:51.285957Z",
     "iopub.status.busy": "2026-09-16T22:03:51.285838Z",
     "iopub.status.idle": "2026-09-16T22:03:51.293777Z",
     "shell.execute_reply": "2026-09-16T22:03:51.293209Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>split</th>\n",
       "      <th>mae_kb</th>\n",
       "      <th>rmse_kb</th>\n",
       "      <th>r2</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>6,781.31</td>\n",
       "      <td>9,036.61</td>\n",
       "      <td>0.44</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>selected_padd_models</td>\n",
       "      <td>holdout</td>\n",
       "      <td>7,250.51</td>\n",
       "      <td>9,222.26</td>\n",
       "      <td>0.42</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>7,339.84</td>\n",
       "      <td>9,248.33</td>\n",
       "      <td>0.42</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>7,862.99</td>\n",
       "      <td>9,562.93</td>\n",
       "      <td>0.38</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>spline_ridge_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>8,336.50</td>\n",
       "      <td>10,238.91</td>\n",
       "      <td>0.29</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ensemble_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>8,574.38</td>\n",
       "      <td>10,287.70</td>\n",
       "      <td>0.28</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>8,896.36</td>\n",
       "      <td>11,143.55</td>\n",
       "      <td>0.15</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>9,011.52</td>\n",
       "      <td>10,533.61</td>\n",
       "      <td>0.24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>seasonal_ridge_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>9,225.54</td>\n",
       "      <td>11,265.22</td>\n",
       "      <td>0.14</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>damped_ridge_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>9,436.51</td>\n",
       "      <td>11,551.45</td>\n",
       "      <td>0.09</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>polynomial_ridge_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>9,509.31</td>\n",
       "      <td>11,410.50</td>\n",
       "      <td>0.11</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>persistence</td>\n",
       "      <td>holdout</td>\n",
       "      <td>9,630.54</td>\n",
       "      <td>12,194.98</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>rolling_ridge_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>9,952.10</td>\n",
       "      <td>12,379.51</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>huber_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>10,015.02</td>\n",
       "      <td>11,610.71</td>\n",
       "      <td>0.08</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ridge_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>10,045.40</td>\n",
       "      <td>12,095.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>constrained_level</td>\n",
       "      <td>holdout</td>\n",
       "      <td>10,755.87</td>\n",
       "      <td>12,128.02</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>holdout</td>\n",
       "      <td>10,990.08</td>\n",
       "      <td>13,923.84</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>forecast_flow_identity</td>\n",
       "      <td>holdout</td>\n",
       "      <td>11,878.49</td>\n",
       "      <td>14,265.31</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       model    split    mae_kb   rmse_kb    r2   n\n",
       "23           seasonal_change  holdout  6,781.31  9,036.61  0.44  24\n",
       "31      selected_padd_models  holdout  7,250.51  9,222.26  0.42  24\n",
       "27  seasonal_residual_change  holdout  7,339.84  9,248.33  0.42  24\n",
       "17      random_forest_change  holdout  7,862.99  9,562.93  0.38  24\n",
       "33       spline_ridge_change  holdout  8,336.50 10,238.91  0.29  24\n",
       "5            ensemble_change  holdout  8,574.38 10,287.70  0.28  24\n",
       "35            xgboost_change  holdout  8,896.36 11,143.55  0.15  24\n",
       "11     neural_network_change  holdout  9,011.52 10,533.61  0.24  24\n",
       "29     seasonal_ridge_change  holdout  9,225.54 11,265.22  0.14  24\n",
       "3        damped_ridge_change  holdout  9,436.51 11,551.45  0.09  24\n",
       "15   polynomial_ridge_change  holdout  9,509.31 11,410.50  0.11  24\n",
       "13               persistence  holdout  9,630.54 12,194.98 -0.01  24\n",
       "21      rolling_ridge_change  holdout  9,952.10 12,379.51 -0.04  24\n",
       "9               huber_change  holdout 10,015.02 11,610.71  0.08  24\n",
       "19              ridge_change  holdout 10,045.40 12,095.97  0.00  24\n",
       "1          constrained_level  holdout 10,755.87 12,128.02 -0.00  24\n",
       "25            seasonal_naive  holdout 10,990.08 13,923.84 -0.32  24\n",
       "7     forecast_flow_identity  holdout 11,878.49 14,265.31 -0.39  24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Report final one-step national MAE for the development-selected model against\n",
    "# persistence. The percentage is 100 * (1 - selected MAE / baseline MAE), so a\n",
    "# negative value means deterioration. Show settings without selecting on holdout.\n",
    "\n",
    "scores=tables['us_model_metrics'].query(\"split=='holdout'\").sort_values('mae_kb')\n",
    "display(scores)\n",
    "lookup=scores.set_index('model')\n",
    "selected=lookup.loc[metadata['one_month_model'],'mae_kb']\n",
    "naive=lookup.loc['persistence','mae_kb']\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a26f0859-selected-finding",
   "metadata": {},
   "source": [
    "**Selected one-month model:** `seasonal_change`. National MAE is **6,781 kb**, versus **9,631 kb** for persistence (**29.6% lower**). Parameters and the model family were selected using development data only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a26f0859-settings",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:51.295177Z",
     "iopub.status.busy": "2026-09-16T22:03:51.295042Z",
     "iopub.status.idle": "2026-09-16T22:03:51.306808Z",
     "shell.execute_reply": "2026-09-16T22:03:51.306285Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>padd</th>\n",
       "      <th>model</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 10}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>seasonal_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>polynomial_ridge_change</td>\n",
       "      <td>{\"polynomialfeatures__degree\": 1, \"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>spline_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 1000, \"splinetransformer__degree\": 2, \"splinetransformer__n_knots\": 3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>{\"max_depth\": 5, \"min_samples_leaf\": 12, \"n_estimators\": 200}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>{\"learning_rate\": 0.01, \"max_depth\": 2, \"n_estimators\": 200, \"reg_lambda\": 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>{\"regressor__mlpregressor__alpha\": 10, \"regressor__mlpregressor__hidden_layer_sizes\": [32, 16]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>rolling_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 1000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>damped_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 10}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>ensemble_change</td>\n",
       "      <td>{\"weights\": [2, 1, 1]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>huber_change</td>\n",
       "      <td>{\"regressor__huberregressor__alpha\": 10, \"regressor__huberregressor__epsilon\": 1.75}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>seasonal_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>polynomial_ridge_change</td>\n",
       "      <td>{\"polynomialfeatures__degree\": 1, \"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>spline_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 1000, \"splinetransformer__degree\": 2, \"splinetransformer__n_knots\": 3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>{\"max_depth\": null, \"min_samples_leaf\": 12, \"n_estimators\": 200}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>{\"learning_rate\": 0.05, \"max_depth\": 4, \"n_estimators\": 100, \"reg_lambda\": 30}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>{\"regressor__mlpregressor__alpha\": 10, \"regressor__mlpregressor__hidden_layer_sizes\": [32, 16]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>rolling_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>damped_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 10}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>ensemble_change</td>\n",
       "      <td>{\"weights\": [2, 1, 1]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>{\"ridge__alpha\": 10}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>huber_change</td>\n",
       "      <td>{\"regressor__huberregressor__alpha\": 10, \"regressor__huberregressor__epsilon\": 1.75}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>seasonal_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>polynomial_ridge_change</td>\n",
       "      <td>{\"polynomialfeatures__degree\": 2, \"ridge__alpha\": 1000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>spline_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 100, \"splinetransformer__degree\": 2, \"splinetransformer__n_knots\": 3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>{\"max_depth\": 5, \"min_samples_leaf\": 5, \"n_estimators\": 200}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>{\"learning_rate\": 0.1, \"max_depth\": 4, \"n_estimators\": 200, \"reg_lambda\": 30}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>{\"regressor__mlpregressor__alpha\": 10, \"regressor__mlpregressor__hidden_layer_sizes\": [32, 16]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>rolling_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 10}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>damped_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3</td>\n",
       "      <td>ensemble_change</td>\n",
       "      <td>{\"weights\": [1, 1, 2]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>{\"ridge__alpha\": 1000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>huber_change</td>\n",
       "      <td>{\"regressor__huberregressor__alpha\": 10, \"regressor__huberregressor__epsilon\": 1.35}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4</td>\n",
       "      <td>ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 1000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4</td>\n",
       "      <td>seasonal_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 1000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4</td>\n",
       "      <td>polynomial_ridge_change</td>\n",
       "      <td>{\"polynomialfeatures__degree\": 2, \"ridge__alpha\": 1000}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>spline_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 1000, \"splinetransformer__degree\": 2, \"splinetransformer__n_knots\": 3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4</td>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>{\"max_depth\": null, \"min_samples_leaf\": 5, \"n_estimators\": 200}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4</td>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>{\"learning_rate\": 0.1, \"max_depth\": 4, \"n_estimators\": 100, \"reg_lambda\": 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4</td>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>{\"regressor__mlpregressor__alpha\": 10, \"regressor__mlpregressor__hidden_layer_sizes\": [32]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4</td>\n",
       "      <td>rolling_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>4</td>\n",
       "      <td>damped_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 10}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4</td>\n",
       "      <td>ensemble_change</td>\n",
       "      <td>{\"weights\": [1, 1, 2]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4</td>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4</td>\n",
       "      <td>huber_change</td>\n",
       "      <td>{\"regressor__huberregressor__alpha\": 10, \"regressor__huberregressor__epsilon\": 1.75}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5</td>\n",
       "      <td>ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5</td>\n",
       "      <td>seasonal_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>5</td>\n",
       "      <td>polynomial_ridge_change</td>\n",
       "      <td>{\"polynomialfeatures__degree\": 1, \"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>5</td>\n",
       "      <td>spline_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 100, \"splinetransformer__degree\": 2, \"splinetransformer__n_knots\": 3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>5</td>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>{\"max_depth\": null, \"min_samples_leaf\": 5, \"n_estimators\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5</td>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>{\"learning_rate\": 0.01, \"max_depth\": 4, \"n_estimators\": 200, \"reg_lambda\": 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>5</td>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>{\"regressor__mlpregressor__alpha\": 10, \"regressor__mlpregressor__hidden_layer_sizes\": [32, 16]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>5</td>\n",
       "      <td>rolling_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>5</td>\n",
       "      <td>damped_ridge_change</td>\n",
       "      <td>{\"ridge__alpha\": 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>5</td>\n",
       "      <td>ensemble_change</td>\n",
       "      <td>{\"weights\": [1, 1, 2]}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>5</td>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>{\"ridge__alpha\": 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>5</td>\n",
       "      <td>huber_change</td>\n",
       "      <td>{\"regressor__huberregressor__alpha\": 10, \"regressor__huberregressor__epsilon\": 1.35}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    padd                     model  \\\n",
       "0      1              ridge_change   \n",
       "1      1     seasonal_ridge_change   \n",
       "2      1   polynomial_ridge_change   \n",
       "3      1       spline_ridge_change   \n",
       "4      1      random_forest_change   \n",
       "5      1            xgboost_change   \n",
       "6      1     neural_network_change   \n",
       "7      1      rolling_ridge_change   \n",
       "8      1       damped_ridge_change   \n",
       "9      1           ensemble_change   \n",
       "10     1  seasonal_residual_change   \n",
       "11     1              huber_change   \n",
       "12     2              ridge_change   \n",
       "13     2     seasonal_ridge_change   \n",
       "14     2   polynomial_ridge_change   \n",
       "15     2       spline_ridge_change   \n",
       "16     2      random_forest_change   \n",
       "17     2            xgboost_change   \n",
       "18     2     neural_network_change   \n",
       "19     2      rolling_ridge_change   \n",
       "20     2       damped_ridge_change   \n",
       "21     2           ensemble_change   \n",
       "22     2  seasonal_residual_change   \n",
       "23     2              huber_change   \n",
       "24     3              ridge_change   \n",
       "25     3     seasonal_ridge_change   \n",
       "26     3   polynomial_ridge_change   \n",
       "27     3       spline_ridge_change   \n",
       "28     3      random_forest_change   \n",
       "29     3            xgboost_change   \n",
       "30     3     neural_network_change   \n",
       "31     3      rolling_ridge_change   \n",
       "32     3       damped_ridge_change   \n",
       "33     3           ensemble_change   \n",
       "34     3  seasonal_residual_change   \n",
       "35     3              huber_change   \n",
       "36     4              ridge_change   \n",
       "37     4     seasonal_ridge_change   \n",
       "38     4   polynomial_ridge_change   \n",
       "39     4       spline_ridge_change   \n",
       "40     4      random_forest_change   \n",
       "41     4            xgboost_change   \n",
       "42     4     neural_network_change   \n",
       "43     4      rolling_ridge_change   \n",
       "44     4       damped_ridge_change   \n",
       "45     4           ensemble_change   \n",
       "46     4  seasonal_residual_change   \n",
       "47     4              huber_change   \n",
       "48     5              ridge_change   \n",
       "49     5     seasonal_ridge_change   \n",
       "50     5   polynomial_ridge_change   \n",
       "51     5       spline_ridge_change   \n",
       "52     5      random_forest_change   \n",
       "53     5            xgboost_change   \n",
       "54     5     neural_network_change   \n",
       "55     5      rolling_ridge_change   \n",
       "56     5       damped_ridge_change   \n",
       "57     5           ensemble_change   \n",
       "58     5  seasonal_residual_change   \n",
       "59     5              huber_change   \n",
       "\n",
       "                                                                                             params  \n",
       "0                                                                              {\"ridge__alpha\": 10}  \n",
       "1                                                                             {\"ridge__alpha\": 100}  \n",
       "2                                            {\"polynomialfeatures__degree\": 1, \"ridge__alpha\": 100}  \n",
       "3           {\"ridge__alpha\": 1000, \"splinetransformer__degree\": 2, \"splinetransformer__n_knots\": 3}  \n",
       "4                                     {\"max_depth\": 5, \"min_samples_leaf\": 12, \"n_estimators\": 200}  \n",
       "5                     {\"learning_rate\": 0.01, \"max_depth\": 2, \"n_estimators\": 200, \"reg_lambda\": 1}  \n",
       "6   {\"regressor__mlpregressor__alpha\": 10, \"regressor__mlpregressor__hidden_layer_sizes\": [32, 16]}  \n",
       "7                                                                            {\"ridge__alpha\": 1000}  \n",
       "8                                                                              {\"ridge__alpha\": 10}  \n",
       "9                                                                            {\"weights\": [2, 1, 1]}  \n",
       "10                                                                            {\"ridge__alpha\": 100}  \n",
       "11             {\"regressor__huberregressor__alpha\": 10, \"regressor__huberregressor__epsilon\": 1.75}  \n",
       "12                                                                            {\"ridge__alpha\": 100}  \n",
       "13                                                                            {\"ridge__alpha\": 100}  \n",
       "14                                           {\"polynomialfeatures__degree\": 1, \"ridge__alpha\": 100}  \n",
       "15          {\"ridge__alpha\": 1000, \"splinetransformer__degree\": 2, \"splinetransformer__n_knots\": 3}  \n",
       "16                                 {\"max_depth\": null, \"min_samples_leaf\": 12, \"n_estimators\": 200}  \n",
       "17                   {\"learning_rate\": 0.05, \"max_depth\": 4, \"n_estimators\": 100, \"reg_lambda\": 30}  \n",
       "18  {\"regressor__mlpregressor__alpha\": 10, \"regressor__mlpregressor__hidden_layer_sizes\": [32, 16]}  \n",
       "19                                                                            {\"ridge__alpha\": 100}  \n",
       "20                                                                             {\"ridge__alpha\": 10}  \n",
       "21                                                                           {\"weights\": [2, 1, 1]}  \n",
       "22                                                                             {\"ridge__alpha\": 10}  \n",
       "23             {\"regressor__huberregressor__alpha\": 10, \"regressor__huberregressor__epsilon\": 1.75}  \n",
       "24                                                                            {\"ridge__alpha\": 100}  \n",
       "25                                                                            {\"ridge__alpha\": 100}  \n",
       "26                                          {\"polynomialfeatures__degree\": 2, \"ridge__alpha\": 1000}  \n",
       "27           {\"ridge__alpha\": 100, \"splinetransformer__degree\": 2, \"splinetransformer__n_knots\": 3}  \n",
       "28                                     {\"max_depth\": 5, \"min_samples_leaf\": 5, \"n_estimators\": 200}  \n",
       "29                    {\"learning_rate\": 0.1, \"max_depth\": 4, \"n_estimators\": 200, \"reg_lambda\": 30}  \n",
       "30  {\"regressor__mlpregressor__alpha\": 10, \"regressor__mlpregressor__hidden_layer_sizes\": [32, 16]}  \n",
       "31                                                                             {\"ridge__alpha\": 10}  \n",
       "32                                                                            {\"ridge__alpha\": 100}  \n",
       "33                                                                           {\"weights\": [1, 1, 2]}  \n",
       "34                                                                           {\"ridge__alpha\": 1000}  \n",
       "35             {\"regressor__huberregressor__alpha\": 10, \"regressor__huberregressor__epsilon\": 1.35}  \n",
       "36                                                                           {\"ridge__alpha\": 1000}  \n",
       "37                                                                           {\"ridge__alpha\": 1000}  \n",
       "38                                          {\"polynomialfeatures__degree\": 2, \"ridge__alpha\": 1000}  \n",
       "39          {\"ridge__alpha\": 1000, \"splinetransformer__degree\": 2, \"splinetransformer__n_knots\": 3}  \n",
       "40                                  {\"max_depth\": null, \"min_samples_leaf\": 5, \"n_estimators\": 200}  \n",
       "41                     {\"learning_rate\": 0.1, \"max_depth\": 4, \"n_estimators\": 100, \"reg_lambda\": 1}  \n",
       "42      {\"regressor__mlpregressor__alpha\": 10, \"regressor__mlpregressor__hidden_layer_sizes\": [32]}  \n",
       "43                                                                            {\"ridge__alpha\": 100}  \n",
       "44                                                                             {\"ridge__alpha\": 10}  \n",
       "45                                                                           {\"weights\": [1, 1, 2]}  \n",
       "46                                                                            {\"ridge__alpha\": 100}  \n",
       "47             {\"regressor__huberregressor__alpha\": 10, \"regressor__huberregressor__epsilon\": 1.75}  \n",
       "48                                                                              {\"ridge__alpha\": 1}  \n",
       "49                                                                            {\"ridge__alpha\": 100}  \n",
       "50                                           {\"polynomialfeatures__degree\": 1, \"ridge__alpha\": 100}  \n",
       "51           {\"ridge__alpha\": 100, \"splinetransformer__degree\": 2, \"splinetransformer__n_knots\": 3}  \n",
       "52                                  {\"max_depth\": null, \"min_samples_leaf\": 5, \"n_estimators\": 100}  \n",
       "53                    {\"learning_rate\": 0.01, \"max_depth\": 4, \"n_estimators\": 200, \"reg_lambda\": 1}  \n",
       "54  {\"regressor__mlpregressor__alpha\": 10, \"regressor__mlpregressor__hidden_layer_sizes\": [32, 16]}  \n",
       "55                                                                            {\"ridge__alpha\": 100}  \n",
       "56                                                                              {\"ridge__alpha\": 1}  \n",
       "57                                                                           {\"weights\": [1, 1, 2]}  \n",
       "58                                                                            {\"ridge__alpha\": 100}  \n",
       "59             {\"regressor__huberregressor__alpha\": 10, \"regressor__huberregressor__epsilon\": 1.35}  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>padd</th>\n",
       "      <th>model</th>\n",
       "      <th>split</th>\n",
       "      <th>mae_kb</th>\n",
       "      <th>rmse_kb</th>\n",
       "      <th>r2</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>constrained_level</td>\n",
       "      <td>holdout</td>\n",
       "      <td>591.62</td>\n",
       "      <td>776.97</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>persistence</td>\n",
       "      <td>holdout</td>\n",
       "      <td>626.88</td>\n",
       "      <td>792.38</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>579.67</td>\n",
       "      <td>772.86</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2</td>\n",
       "      <td>constrained_level</td>\n",
       "      <td>holdout</td>\n",
       "      <td>3,333.58</td>\n",
       "      <td>4,041.28</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2</td>\n",
       "      <td>persistence</td>\n",
       "      <td>holdout</td>\n",
       "      <td>2,859.67</td>\n",
       "      <td>3,670.33</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>2,879.95</td>\n",
       "      <td>3,729.80</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>3</td>\n",
       "      <td>constrained_level</td>\n",
       "      <td>holdout</td>\n",
       "      <td>7,368.32</td>\n",
       "      <td>9,095.45</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>3</td>\n",
       "      <td>persistence</td>\n",
       "      <td>holdout</td>\n",
       "      <td>7,646.62</td>\n",
       "      <td>9,277.82</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>3</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>5,950.43</td>\n",
       "      <td>7,639.34</td>\n",
       "      <td>0.26</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>4</td>\n",
       "      <td>constrained_level</td>\n",
       "      <td>holdout</td>\n",
       "      <td>664.08</td>\n",
       "      <td>790.47</td>\n",
       "      <td>0.42</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>4</td>\n",
       "      <td>persistence</td>\n",
       "      <td>holdout</td>\n",
       "      <td>681.88</td>\n",
       "      <td>821.33</td>\n",
       "      <td>0.38</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>4</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>556.01</td>\n",
       "      <td>815.43</td>\n",
       "      <td>0.39</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>5</td>\n",
       "      <td>constrained_level</td>\n",
       "      <td>holdout</td>\n",
       "      <td>1,916.97</td>\n",
       "      <td>2,498.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>5</td>\n",
       "      <td>persistence</td>\n",
       "      <td>holdout</td>\n",
       "      <td>1,964.42</td>\n",
       "      <td>2,519.89</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>5</td>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>holdout</td>\n",
       "      <td>1,971.54</td>\n",
       "      <td>2,545.46</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     padd              model    split   mae_kb  rmse_kb    r2   n\n",
       "1       1  constrained_level  holdout   591.62   776.97 -0.15  24\n",
       "13      1        persistence  holdout   626.88   792.38 -0.20  24\n",
       "23      1    seasonal_change  holdout   579.67   772.86 -0.14  24\n",
       "37      2  constrained_level  holdout 3,333.58 4,041.28 -0.41  24\n",
       "49      2        persistence  holdout 2,859.67 3,670.33 -0.17  24\n",
       "59      2    seasonal_change  holdout 2,879.95 3,729.80 -0.20  24\n",
       "73      3  constrained_level  holdout 7,368.32 9,095.45 -0.05  24\n",
       "85      3        persistence  holdout 7,646.62 9,277.82 -0.09  24\n",
       "95      3    seasonal_change  holdout 5,950.43 7,639.34  0.26  24\n",
       "109     4  constrained_level  holdout   664.08   790.47  0.42  24\n",
       "121     4        persistence  holdout   681.88   821.33  0.38  24\n",
       "131     4    seasonal_change  holdout   556.01   815.43  0.39  24\n",
       "145     5  constrained_level  holdout 1,916.97 2,498.38 -0.39  24\n",
       "157     5        persistence  holdout 1,964.42 2,519.89 -0.41  24\n",
       "167     5    seasonal_change  holdout 1,971.54 2,545.46 -0.44  24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context('display.max_colwidth',None,'display.max_rows',None):\n",
    "    display(tables['best_parameters'].query(\"params != '{}'\").reset_index(drop=True))\n",
    "comparison_names = [metadata['one_month_model'], 'constrained_level', 'persistence']\n",
    "display(tables['padd_model_metrics'].query(\"split == 'holdout' and model in @comparison_names\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fb1f317b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:51.308278Z",
     "iopub.status.busy": "2026-09-16T22:03:51.308169Z",
     "iopub.status.idle": "2026-09-16T22:03:51.323723Z",
     "shell.execute_reply": "2026-09-16T22:03:51.323089Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_target_months</th>\n",
       "      <th>eligible_stock_training_months</th>\n",
       "      <th>evaluation_months</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>padd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>222</td>\n",
       "      <td>197</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>222</td>\n",
       "      <td>197</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>222</td>\n",
       "      <td>197</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>222</td>\n",
       "      <td>197</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>222</td>\n",
       "      <td>197</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      total_target_months  eligible_stock_training_months  evaluation_months\n",
       "padd                                                                        \n",
       "1                     222                             197                209\n",
       "2                     222                             197                209\n",
       "3                     222                             197                209\n",
       "4                     222                             197                209\n",
       "5                     222                             197                209"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>previous_mae_kb</th>\n",
       "      <th>refit_mae_kb</th>\n",
       "      <th>improvement_pct</th>\n",
       "      <th>test_months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U.S.</td>\n",
       "      <td>6,556.73</td>\n",
       "      <td>6,781.31</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PADD 1</td>\n",
       "      <td>676.88</td>\n",
       "      <td>579.67</td>\n",
       "      <td>14.36</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PADD 2</td>\n",
       "      <td>3,143.41</td>\n",
       "      <td>2,879.95</td>\n",
       "      <td>8.38</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PADD 3</td>\n",
       "      <td>5,740.99</td>\n",
       "      <td>5,950.43</td>\n",
       "      <td>-3.65</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PADD 4</td>\n",
       "      <td>510.71</td>\n",
       "      <td>556.01</td>\n",
       "      <td>-8.87</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PADD 5</td>\n",
       "      <td>2,086.72</td>\n",
       "      <td>1,971.54</td>\n",
       "      <td>5.52</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  previous_mae_kb  refit_mae_kb  improvement_pct  test_months\n",
       "0    U.S.         6,556.73      6,781.31            -3.43           24\n",
       "1  PADD 1           676.88        579.67            14.36           24\n",
       "2  PADD 2         3,143.41      2,879.95             8.38           24\n",
       "3  PADD 3         5,740.99      5,950.43            -3.65           24\n",
       "4  PADD 4           510.71        556.01            -8.87           24\n",
       "5  PADD 5         2,086.72      1,971.54             5.52           24"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>candidate_set</th>\n",
       "      <th>period</th>\n",
       "      <th>mae_kb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Original candidates, COVID excluded</td>\n",
       "      <td>cv</td>\n",
       "      <td>8,190.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Original candidates, COVID excluded</td>\n",
       "      <td>holdout</td>\n",
       "      <td>6,781.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Expanded candidates, COVID excluded</td>\n",
       "      <td>cv</td>\n",
       "      <td>8,190.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Expanded candidates, COVID excluded</td>\n",
       "      <td>holdout</td>\n",
       "      <td>6,781.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         candidate_set   period   mae_kb\n",
       "0  Original candidates, COVID excluded       cv 8,190.01\n",
       "1  Original candidates, COVID excluded  holdout 6,781.31\n",
       "2  Expanded candidates, COVID excluded       cv 8,190.01\n",
       "3  Expanded candidates, COVID excluded  holdout 6,781.31"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize eligible dates and check that fitting excludes COVID target months.\n",
    "# Optional comparison CSVs show earlier refits on matching dates when available.\n",
    "\n",
    "audit = tables['training_sample_audit']\n",
    "display(audit.groupby('padd').agg(total_target_months=('training_eligible','size'),\n",
    "    eligible_stock_training_months=('training_eligible','sum'), evaluation_months=('evaluation_eligible','sum')))\n",
    "assert not audit.loc[audit.training_eligible, 'month'].between(COVID_START, COVID_END).any()\n",
    "# Display prior-run comparison files only when available; these optional reports\n",
    "# are read here and are not recomputed by this cell.\n",
    "comparison_file = OUTPUT/'refit_comparison.csv'\n",
    "if comparison_file.exists():\n",
    "    display(pd.read_csv(comparison_file))\n",
    "addition_file = OUTPUT/'candidate_addition_comparison.csv'\n",
    "if addition_file.exists():\n",
    "    display(pd.read_csv(addition_file))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb1f317b-year-heading",
   "metadata": {},
   "source": [
    "**Year-ahead comparison on the same forecast dates and origins**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fb1f317b-year-comparison",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:51.325095Z",
     "iopub.status.busy": "2026-09-16T22:03:51.324960Z",
     "iopub.status.idle": "2026-09-16T22:03:51.331132Z",
     "shell.execute_reply": "2026-09-16T22:03:51.330457Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>previous_mae_kb</th>\n",
       "      <th>refit_mae_kb</th>\n",
       "      <th>improvement_pct</th>\n",
       "      <th>forecast_observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U.S.</td>\n",
       "      <td>9,111.59</td>\n",
       "      <td>9,055.39</td>\n",
       "      <td>0.62</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PADD 1</td>\n",
       "      <td>875.61</td>\n",
       "      <td>671.05</td>\n",
       "      <td>23.36</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PADD 2</td>\n",
       "      <td>4,033.29</td>\n",
       "      <td>3,004.13</td>\n",
       "      <td>25.52</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PADD 3</td>\n",
       "      <td>7,848.24</td>\n",
       "      <td>7,194.17</td>\n",
       "      <td>8.33</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PADD 4</td>\n",
       "      <td>667.23</td>\n",
       "      <td>840.92</td>\n",
       "      <td>-26.03</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PADD 5</td>\n",
       "      <td>2,204.30</td>\n",
       "      <td>1,382.37</td>\n",
       "      <td>37.29</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  previous_mae_kb  refit_mae_kb  improvement_pct  \\\n",
       "0    U.S.         9,111.59      9,055.39             0.62   \n",
       "1  PADD 1           875.61        671.05            23.36   \n",
       "2  PADD 2         4,033.29      3,004.13            25.52   \n",
       "3  PADD 3         7,848.24      7,194.17             8.33   \n",
       "4  PADD 4           667.23        840.92           -26.03   \n",
       "5  PADD 5         2,204.30      1,382.37            37.29   \n",
       "\n",
       "   forecast_observations  \n",
       "0                    156  \n",
       "1                    156  \n",
       "2                    156  \n",
       "3                    156  \n",
       "4                    156  \n",
       "5                    156  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_comparison = OUTPUT/'year_ahead_refit_comparison.csv'\n",
    "if year_comparison.exists():\n",
    "    display(pd.read_csv(year_comparison))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fd98cf5",
   "metadata": {},
   "source": [
    "### Regional results\n",
    "\n",
    "Compare each PADD with unchanged stocks. Positive improvement indicates lower MAE, positive bias indicates stocks were overestimated. U.S. errors are calculated after summing regional forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7a5107fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:51.332632Z",
     "iopub.status.busy": "2026-09-16T22:03:51.332467Z",
     "iopub.status.idle": "2026-09-16T22:03:51.795736Z",
     "shell.execute_reply": "2026-09-16T22:03:51.794912Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PADD</th>\n",
       "      <th>Average error (kb)</th>\n",
       "      <th>Error / average stocks (%)</th>\n",
       "      <th>Improvement over unchanged stocks (%)</th>\n",
       "      <th>Average bias (kb)</th>\n",
       "      <th>Worst month</th>\n",
       "      <th>Largest miss (kb)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>579.67</td>\n",
       "      <td>7.12</td>\n",
       "      <td>7.53</td>\n",
       "      <td>-136.53</td>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>2,265.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2,879.95</td>\n",
       "      <td>2.72</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>224.34</td>\n",
       "      <td>2026-05-01</td>\n",
       "      <td>9,159.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5,950.43</td>\n",
       "      <td>2.49</td>\n",
       "      <td>22.18</td>\n",
       "      <td>599.34</td>\n",
       "      <td>2026-02-01</td>\n",
       "      <td>17,923.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>556.01</td>\n",
       "      <td>2.33</td>\n",
       "      <td>18.46</td>\n",
       "      <td>-12.15</td>\n",
       "      <td>2026-05-01</td>\n",
       "      <td>2,233.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1,971.54</td>\n",
       "      <td>4.23</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>131.87</td>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>5,824.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PADD  Average error (kb)  Error / average stocks (%)  \\\n",
       "0     1              579.67                        7.12   \n",
       "1     2            2,879.95                        2.72   \n",
       "2     3            5,950.43                        2.49   \n",
       "3     4              556.01                        2.33   \n",
       "4     5            1,971.54                        4.23   \n",
       "\n",
       "   Improvement over unchanged stocks (%)  Average bias (kb) Worst month  \\\n",
       "0                                   7.53            -136.53  2024-07-01   \n",
       "1                                  -0.71             224.34  2026-05-01   \n",
       "2                                  22.18             599.34  2026-02-01   \n",
       "3                                  18.46             -12.15  2026-05-01   \n",
       "4                                  -0.36             131.87  2024-07-01   \n",
       "\n",
       "   Largest miss (kb)  \n",
       "0           2,265.20  \n",
       "1           9,159.40  \n",
       "2          17,923.00  \n",
       "3           2,233.80  \n",
       "4           5,824.20  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare regional selected-model errors with unchanged stocks on the same\n",
    "# holdout dates. Normalize MAE by average actual stocks to compare PADD sizes;\n",
    "# positive percentage improvement means lower error than the benchmark.\n",
    "\n",
    "# Compare like-for-like regional errors on the same final 24 months.\n",
    "regional_test = tables['padd_predictions'].query(\"split == 'holdout'\")\n",
    "selected_names = tables['selected_models'][['padd','selected_model']]\n",
    "regional_selected = regional_test.merge(selected_names, on='padd')\n",
    "regional_selected = regional_selected[regional_selected.model.eq(regional_selected.selected_model)]\n",
    "rows = []\n",
    "for padd, g in regional_selected.groupby('padd'):\n",
    "    baseline = regional_test[regional_test.padd.eq(padd) & regional_test.model.eq('persistence')]\n",
    "    # Align selected and persistence forecasts on the same month within this PADD.\n",
    "    # Validate unique matches so benchmark improvement is a like-for-like comparison.\n",
    "    paired = g.merge(baseline[['month','predicted_kb']], on='month', suffixes=('', '_unchanged'), validate='one_to_one')\n",
    "    # Positive signed error means overestimated stocks; absolute error measures\n",
    "    # miss size, while mean signed error below measures systematic bias.\n",
    "    error = paired.predicted_kb - paired.actual_kb\n",
    "    baseline_mae = (paired.predicted_kb_unchanged - paired.actual_kb).abs().mean()\n",
    "    # Keep the row label of the largest absolute miss to retrieve its calendar month.\n",
    "    worst = error.abs().idxmax()\n",
    "    rows.append({'PADD': padd, 'Average error (kb)': error.abs().mean(),\n",
    "        'Error / average stocks (%)': 100 * error.abs().mean() / paired.actual_kb.mean(),\n",
    "        'Improvement over unchanged stocks (%)': 100 * (1 - error.abs().mean() / baseline_mae),\n",
    "        'Average bias (kb)': error.mean(), 'Worst month': paired.loc[worst, 'month'],\n",
    "        'Largest miss (kb)': error.abs().max()})\n",
    "regional_review = pd.DataFrame(rows)\n",
    "display(regional_review)\n",
    "# Persist regional diagnostics; charts convert kb to million barrels by /1000.\n",
    "regional_review.to_csv(OUTPUT/'regional_error_review.csv', index=False)\n",
    "fig, axes = plt.subplots(5, 1, figsize=(12, 10), sharex=True)\n",
    "for ax, (padd, g) in zip(axes, regional_selected.groupby('padd')):\n",
    "    ax.bar(g.month, (g.predicted_kb-g.actual_kb)/1000, width=20)\n",
    "    ax.axhline(0, color='black', linewidth=.6)\n",
    "    ax.set_ylabel(f'PADD {padd}\\nmillion bbl')\n",
    "    ax.set_title(g.selected_model.iloc[0], fontsize=9, loc='right')\n",
    "axes[0].set_title('Monthly inventory forecast errors: positive means stocks were overestimated')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8aef85ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:51.798010Z",
     "iopub.status.busy": "2026-09-16T22:03:51.797796Z",
     "iopub.status.idle": "2026-09-16T22:03:52.005242Z",
     "shell.execute_reply": "2026-09-16T22:03:52.004594Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use one model's actual column to plot national stocks once, then overlay\n",
    "# candidate forecasts and signed selected-model errors. Divide kb by 1000\n",
    "# for million-barrel axes; a positive error bar means excess predicted stocks.\n",
    "\n",
    "hold=tables['us_predictions'].query(\"split=='holdout'\")\n",
    "one_month_model = metadata['one_month_model']\n",
    "actual=hold[hold.model.eq(one_month_model)]\n",
    "fig,axes=plt.subplots(2,1,figsize=(12,7),sharex=True)\n",
    "axes[0].plot(actual.month,actual.actual_kb/1000,color='black',linewidth=2,label='Actual')\n",
    "for name in [one_month_model,'constrained_level','persistence']:\n",
    "    g=hold[hold.model.eq(name)]\n",
    "    axes[0].plot(g.month,g.predicted_kb/1000,label=name,alpha=.8)\n",
    "axes[0].set(title='Final one-month evaluation: U.S. commercial crude stocks',ylabel='Million barrels');axes[0].legend()\n",
    "axes[1].bar(actual.month,(actual.predicted_kb-actual.actual_kb)/1000,width=20)\n",
    "axes[1].axhline(0,color='black');axes[1].set(ylabel='Forecast − actual (million bbl)',title=f'{one_month_model} forecast errors')\n",
    "plt.tight_layout();plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bf719703",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:52.007130Z",
     "iopub.status.busy": "2026-09-16T22:03:52.006867Z",
     "iopub.status.idle": "2026-09-16T22:03:52.111260Z",
     "shell.execute_reply": "2026-09-16T22:03:52.110801Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mae_kb</th>\n",
       "      <th>rmse_kb</th>\n",
       "      <th>r2</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>constrained_level</td>\n",
       "      <td>28,781.81</td>\n",
       "      <td>38,347.05</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>xgboost_change</td>\n",
       "      <td>28,919.88</td>\n",
       "      <td>39,424.45</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>damped_ridge_change</td>\n",
       "      <td>29,341.30</td>\n",
       "      <td>37,692.35</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>random_forest_change</td>\n",
       "      <td>29,822.63</td>\n",
       "      <td>42,305.12</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ensemble_change</td>\n",
       "      <td>29,866.45</td>\n",
       "      <td>39,904.73</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>persistence</td>\n",
       "      <td>29,870.10</td>\n",
       "      <td>36,728.66</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>seasonal_residual_change</td>\n",
       "      <td>30,349.95</td>\n",
       "      <td>40,776.48</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>seasonal_change</td>\n",
       "      <td>30,562.80</td>\n",
       "      <td>42,345.68</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>neural_network_change</td>\n",
       "      <td>30,788.51</td>\n",
       "      <td>41,904.48</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>seasonal_ridge_change</td>\n",
       "      <td>31,776.08</td>\n",
       "      <td>41,092.61</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ridge_change</td>\n",
       "      <td>32,365.60</td>\n",
       "      <td>41,160.37</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>spline_ridge_change</td>\n",
       "      <td>32,811.55</td>\n",
       "      <td>41,439.32</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>rolling_ridge_change</td>\n",
       "      <td>33,019.08</td>\n",
       "      <td>43,972.66</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>polynomial_ridge_change</td>\n",
       "      <td>33,082.74</td>\n",
       "      <td>42,960.75</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>huber_change</td>\n",
       "      <td>34,169.66</td>\n",
       "      <td>44,236.48</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>seasonal_naive</td>\n",
       "      <td>40,506.54</td>\n",
       "      <td>49,476.85</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>forecast_flow_identity</td>\n",
       "      <td>68,966.32</td>\n",
       "      <td>86,352.95</td>\n",
       "      <td>-6.67</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       model    mae_kb   rmse_kb    r2   n\n",
       "0          constrained_level 28,781.81 38,347.05 -0.51  72\n",
       "1             xgboost_change 28,919.88 39,424.45 -0.60  72\n",
       "2        damped_ridge_change 29,341.30 37,692.35 -0.46  72\n",
       "3       random_forest_change 29,822.63 42,305.12 -0.84  72\n",
       "4            ensemble_change 29,866.45 39,904.73 -0.64  72\n",
       "5                persistence 29,870.10 36,728.66 -0.39  72\n",
       "6   seasonal_residual_change 30,349.95 40,776.48 -0.71  72\n",
       "7            seasonal_change 30,562.80 42,345.68 -0.84  72\n",
       "8      neural_network_change 30,788.51 41,904.48 -0.81  72\n",
       "9      seasonal_ridge_change 31,776.08 41,092.61 -0.74  72\n",
       "10              ridge_change 32,365.60 41,160.37 -0.74  72\n",
       "11       spline_ridge_change 32,811.55 41,439.32 -0.77  72\n",
       "12      rolling_ridge_change 33,019.08 43,972.66 -0.99  72\n",
       "13   polynomial_ridge_change 33,082.74 42,960.75 -0.90  72\n",
       "14              huber_change 34,169.66 44,236.48 -1.01  72\n",
       "15            seasonal_naive 40,506.54 49,476.85 -1.52  72\n",
       "16    forecast_flow_identity 68,966.32 86,352.95 -6.67  72"
      ]
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       "      <th>first_target</th>\n",
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       "    <tr>\n",
       "      <th>origin_month</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-06-01</th>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>2017-06-01</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>2018-06-01</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>2019-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2022-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>2023-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-01</th>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>2024-06-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "             first_target last_target\n",
       "origin_month                         \n",
       "2016-06-01     2016-07-01  2017-06-01\n",
       "2017-06-01     2017-07-01  2018-06-01\n",
       "2018-06-01     2018-07-01  2019-06-01\n",
       "2021-06-01     2021-07-01  2022-06-01\n",
       "2022-06-01     2022-07-01  2023-06-01\n",
       "2023-06-01     2023-07-01  2024-06-01"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>constrained_level</th>\n",
       "      <td>9,055.39</td>\n",
       "      <td>7,937.23</td>\n",
       "      <td>29,209.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>persistence</th>\n",
       "      <td>10,952.65</td>\n",
       "      <td>9,668.00</td>\n",
       "      <td>39,174.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_change</th>\n",
       "      <td>11,313.40</td>\n",
       "      <td>10,463.60</td>\n",
       "      <td>41,696.80</td>\n",
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      "text/plain": [
       "                       mean    median       max\n",
       "model                                          \n",
       "constrained_level  9,055.39  7,937.23 29,209.14\n",
       "persistence       10,952.65  9,668.00 39,174.00\n",
       "seasonal_change   11,313.40 10,463.60 41,696.80"
      ]
     },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model</th>\n",
       "      <th>constrained_level</th>\n",
       "      <th>persistence</th>\n",
       "      <th>seasonal_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horizon</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7,795.34</td>\n",
       "      <td>7,190.69</td>\n",
       "      <td>3,954.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8,871.62</td>\n",
       "      <td>10,117.62</td>\n",
       "      <td>6,913.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9,625.49</td>\n",
       "      <td>10,605.85</td>\n",
       "      <td>8,737.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9,610.92</td>\n",
       "      <td>12,202.69</td>\n",
       "      <td>9,926.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8,631.78</td>\n",
       "      <td>12,647.62</td>\n",
       "      <td>11,138.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7,784.86</td>\n",
       "      <td>11,781.00</td>\n",
       "      <td>12,218.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8,533.26</td>\n",
       "      <td>12,079.77</td>\n",
       "      <td>11,995.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9,323.87</td>\n",
       "      <td>12,631.77</td>\n",
       "      <td>12,896.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10,022.13</td>\n",
       "      <td>11,347.77</td>\n",
       "      <td>14,962.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9,838.57</td>\n",
       "      <td>11,566.15</td>\n",
       "      <td>16,221.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8,981.00</td>\n",
       "      <td>10,312.23</td>\n",
       "      <td>14,879.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9,645.86</td>\n",
       "      <td>8,948.62</td>\n",
       "      <td>11,916.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model    constrained_level  persistence  seasonal_change\n",
       "horizon                                                 \n",
       "1                 7,795.34     7,190.69         3,954.18\n",
       "2                 8,871.62    10,117.62         6,913.70\n",
       "3                 9,625.49    10,605.85         8,737.63\n",
       "4                 9,610.92    12,202.69         9,926.27\n",
       "5                 8,631.78    12,647.62        11,138.46\n",
       "6                 7,784.86    11,781.00        12,218.46\n",
       "7                 8,533.26    12,079.77        11,995.90\n",
       "8                 9,323.87    12,631.77        12,896.13\n",
       "9                10,022.13    11,347.77        14,962.32\n",
       "10                9,838.57    11,566.15        16,221.69\n",
       "11                8,981.00    10,312.23        14,879.82\n",
       "12                9,645.86     8,948.62        11,916.30"
      ]
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",
      "text/plain": [
       "<Figure size 1100x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check recursive development targets precede the holdout, then summarize final\n",
    "# recursive errors across paths and by lead month. Later horizons repeatedly\n",
    "# use predicted history, unlike the actual-prior-input one-step evaluation.\n",
    "\n",
    "display(tables['year_ahead_model_cv_metrics'])\n",
    "cv_paths=tables['us_recursive_cv_predictions']\n",
    "display(cv_paths[cv_paths.model.eq('persistence')].groupby('origin_month').agg(first_target=('month','min'),last_target=('month','max')))\n",
    "assert cv_paths.month.max()<pd.Timestamp(metadata['holdout_start'])\n",
    "recursive=tables['us_recursive_holdout_predictions'].copy()\n",
    "recursive['abs_error_kb']=(recursive.predicted_kb-recursive.actual_kb).abs()\n",
    "recursive_summary=recursive.groupby('model').abs_error_kb.agg(['mean','median','max'])\n",
    "display(recursive_summary)\n",
    "horizon=tables['us_recursive_horizon_metrics']\n",
    "display(horizon.pivot(index='horizon',columns='model',values='mae_kb'))\n",
    "horizon.pivot(index='horizon',columns='model',values='mae_kb').plot(figsize=(11,4),marker='o',title='Final recursive error by forecast horizon')\n",
    "plt.ylabel('National MAE (kb)');plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8b5c97b-long-limitation",
   "metadata": {},
   "source": [
    "**Long-horizon limitation:** The development-selected `constrained_level` model has recursive final-period MAE of **9,055 kb**, versus **10,953 kb** for persistence. Historical development selection does not guarantee better forecasts after a model or market regime changes. The selected model beats persistence on these correlated evaluation paths; this is a retrospective result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c8b5c97b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:52.112471Z",
     "iopub.status.busy": "2026-09-16T22:03:52.112345Z",
     "iopub.status.idle": "2026-09-16T22:03:52.273239Z",
     "shell.execute_reply": "2026-09-16T22:03:52.272737Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare recursive holdout MAE with persistence and plot the earliest/latest\n",
    "# forecast origins as examples. These overlapping paths are correlated evidence,\n",
    "# so the observed comparison is not a guarantee of future year-ahead skill.\n",
    "\n",
    "recursive_mae=recursive_summary.loc[metadata['year_ahead_model'],'mean']\n",
    "persistence_mae=recursive_summary.loc['persistence','mean']\n",
    "\n",
    "fig,axes=plt.subplots(1,2,figsize=(14,4))\n",
    "origins=sorted(recursive.origin_month.unique())\n",
    "for origin,ax in zip([origins[0],origins[-1]],axes):\n",
    "    g=recursive[recursive.origin_month.eq(origin)]\n",
    "    a=g[g.model.eq(metadata['year_ahead_model'])]\n",
    "    ax.plot(a.month,a.actual_kb/1000,color='black',label='Actual')\n",
    "    comparison_models = list(dict.fromkeys([metadata['year_ahead_model'], metadata['one_month_model'], 'persistence']))\n",
    "    for name in comparison_models:\n",
    "        t=g[g.model.eq(name)];ax.plot(t.month,t.predicted_kb/1000,label=name)\n",
    "    ax.set(title=f'Origin {pd.Timestamp(origin):%Y-%m}',ylabel='Million barrels');ax.legend(fontsize=8)\n",
    "plt.tight_layout();plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b64f473",
   "metadata": {},
   "source": [
    "### Twelve-month outlook\n",
    "\n",
    "Predict supply, disposition, and commercial stocks for July 2026–June 2027 from data through June 2026.\n",
    "\n",
    "The difference between the statistical stock change and projected net flows is reported as model reconciliation, not an EIA adjustment. \n",
    "\n",
    "One-month and twelve-month forecasts use separately selected models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0eda3f4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:52.274606Z",
     "iopub.status.busy": "2026-09-16T22:03:52.274485Z",
     "iopub.status.idle": "2026-09-16T22:03:52.914722Z",
     "shell.execute_reply": "2026-09-16T22:03:52.914307Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>production_kbd</th>\n",
       "      <th>demand_kbd</th>\n",
       "      <th>imports_kbd</th>\n",
       "      <th>exports_kbd</th>\n",
       "      <th>transfers_kbd</th>\n",
       "      <th>adjustments_kbd</th>\n",
       "      <th>cdu_capacity_kbd</th>\n",
       "      <th>utilization_ratio</th>\n",
       "      <th>balance_kbd</th>\n",
       "      <th>stock_kb</th>\n",
       "      <th>identity_stock_unclipped_kb</th>\n",
       "      <th>model_reconciliation_kbd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2026-07-01</td>\n",
       "      <td>14,241.61</td>\n",
       "      <td>16,880.64</td>\n",
       "      <td>6,514.40</td>\n",
       "      <td>4,375.73</td>\n",
       "      <td>660.18</td>\n",
       "      <td>-34.30</td>\n",
       "      <td>18,027.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>125.51</td>\n",
       "      <td>402,441.45</td>\n",
       "      <td>408,011.76</td>\n",
       "      <td>-179.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2026-08-01</td>\n",
       "      <td>14,323.07</td>\n",
       "      <td>16,915.37</td>\n",
       "      <td>6,399.69</td>\n",
       "      <td>4,551.10</td>\n",
       "      <td>725.39</td>\n",
       "      <td>1.74</td>\n",
       "      <td>18,027.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-16.58</td>\n",
       "      <td>407,121.62</td>\n",
       "      <td>407,497.70</td>\n",
       "      <td>167.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2026-09-01</td>\n",
       "      <td>14,322.92</td>\n",
       "      <td>16,436.70</td>\n",
       "      <td>6,432.55</td>\n",
       "      <td>4,469.70</td>\n",
       "      <td>775.57</td>\n",
       "      <td>-372.50</td>\n",
       "      <td>18,027.00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>252.12</td>\n",
       "      <td>410,311.82</td>\n",
       "      <td>415,061.35</td>\n",
       "      <td>-145.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2026-10-01</td>\n",
       "      <td>14,555.87</td>\n",
       "      <td>15,956.04</td>\n",
       "      <td>6,084.56</td>\n",
       "      <td>4,598.81</td>\n",
       "      <td>800.83</td>\n",
       "      <td>-200.75</td>\n",
       "      <td>18,027.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>685.65</td>\n",
       "      <td>412,584.02</td>\n",
       "      <td>436,316.35</td>\n",
       "      <td>-612.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2026-11-01</td>\n",
       "      <td>14,599.41</td>\n",
       "      <td>16,652.84</td>\n",
       "      <td>6,340.41</td>\n",
       "      <td>4,624.04</td>\n",
       "      <td>713.67</td>\n",
       "      <td>-124.50</td>\n",
       "      <td>18,027.00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>252.11</td>\n",
       "      <td>418,198.74</td>\n",
       "      <td>443,879.74</td>\n",
       "      <td>-64.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2026-12-01</td>\n",
       "      <td>14,501.79</td>\n",
       "      <td>16,725.74</td>\n",
       "      <td>6,330.31</td>\n",
       "      <td>4,687.29</td>\n",
       "      <td>703.69</td>\n",
       "      <td>-64.41</td>\n",
       "      <td>18,027.00</td>\n",
       "      <td>0.93</td>\n",
       "      <td>58.33</td>\n",
       "      <td>420,626.06</td>\n",
       "      <td>445,688.02</td>\n",
       "      <td>19.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2027-01-01</td>\n",
       "      <td>14,199.84</td>\n",
       "      <td>15,993.16</td>\n",
       "      <td>6,454.74</td>\n",
       "      <td>4,473.16</td>\n",
       "      <td>733.89</td>\n",
       "      <td>-323.94</td>\n",
       "      <td>18,027.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>598.23</td>\n",
       "      <td>422,217.11</td>\n",
       "      <td>464,233.08</td>\n",
       "      <td>-546.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2027-02-01</td>\n",
       "      <td>14,430.85</td>\n",
       "      <td>15,725.85</td>\n",
       "      <td>6,332.03</td>\n",
       "      <td>4,873.69</td>\n",
       "      <td>781.21</td>\n",
       "      <td>-70.78</td>\n",
       "      <td>18,027.00</td>\n",
       "      <td>0.87</td>\n",
       "      <td>873.76</td>\n",
       "      <td>425,118.40</td>\n",
       "      <td>488,698.47</td>\n",
       "      <td>-770.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2027-03-01</td>\n",
       "      <td>14,617.63</td>\n",
       "      <td>16,309.55</td>\n",
       "      <td>6,204.73</td>\n",
       "      <td>4,755.97</td>\n",
       "      <td>774.71</td>\n",
       "      <td>-63.35</td>\n",
       "      <td>18,027.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>468.21</td>\n",
       "      <td>430,310.96</td>\n",
       "      <td>503,212.91</td>\n",
       "      <td>-300.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2027-04-01</td>\n",
       "      <td>14,661.55</td>\n",
       "      <td>16,353.71</td>\n",
       "      <td>6,196.55</td>\n",
       "      <td>4,974.10</td>\n",
       "      <td>746.39</td>\n",
       "      <td>-54.83</td>\n",
       "      <td>18,027.00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>221.84</td>\n",
       "      <td>431,980.62</td>\n",
       "      <td>509,868.05</td>\n",
       "      <td>-166.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2027-05-01</td>\n",
       "      <td>14,586.15</td>\n",
       "      <td>16,963.55</td>\n",
       "      <td>6,343.89</td>\n",
       "      <td>4,903.26</td>\n",
       "      <td>821.36</td>\n",
       "      <td>-174.17</td>\n",
       "      <td>18,027.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-289.58</td>\n",
       "      <td>433,010.61</td>\n",
       "      <td>500,890.94</td>\n",
       "      <td>322.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2027-06-01</td>\n",
       "      <td>14,661.15</td>\n",
       "      <td>17,218.61</td>\n",
       "      <td>6,343.21</td>\n",
       "      <td>4,766.78</td>\n",
       "      <td>747.89</td>\n",
       "      <td>-153.16</td>\n",
       "      <td>18,027.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>-386.30</td>\n",
       "      <td>430,462.99</td>\n",
       "      <td>489,302.00</td>\n",
       "      <td>301.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        month  production_kbd  demand_kbd  imports_kbd  exports_kbd  \\\n",
       "0  2026-07-01       14,241.61   16,880.64     6,514.40     4,375.73   \n",
       "1  2026-08-01       14,323.07   16,915.37     6,399.69     4,551.10   \n",
       "2  2026-09-01       14,322.92   16,436.70     6,432.55     4,469.70   \n",
       "3  2026-10-01       14,555.87   15,956.04     6,084.56     4,598.81   \n",
       "4  2026-11-01       14,599.41   16,652.84     6,340.41     4,624.04   \n",
       "5  2026-12-01       14,501.79   16,725.74     6,330.31     4,687.29   \n",
       "6  2027-01-01       14,199.84   15,993.16     6,454.74     4,473.16   \n",
       "7  2027-02-01       14,430.85   15,725.85     6,332.03     4,873.69   \n",
       "8  2027-03-01       14,617.63   16,309.55     6,204.73     4,755.97   \n",
       "9  2027-04-01       14,661.55   16,353.71     6,196.55     4,974.10   \n",
       "10 2027-05-01       14,586.15   16,963.55     6,343.89     4,903.26   \n",
       "11 2027-06-01       14,661.15   17,218.61     6,343.21     4,766.78   \n",
       "\n",
       "    transfers_kbd  adjustments_kbd  cdu_capacity_kbd  utilization_ratio  \\\n",
       "0          660.18           -34.30         18,027.00               0.94   \n",
       "1          725.39             1.74         18,027.00               0.94   \n",
       "2          775.57          -372.50         18,027.00               0.91   \n",
       "3          800.83          -200.75         18,027.00               0.89   \n",
       "4          713.67          -124.50         18,027.00               0.92   \n",
       "5          703.69           -64.41         18,027.00               0.93   \n",
       "6          733.89          -323.94         18,027.00               0.89   \n",
       "7          781.21           -70.78         18,027.00               0.87   \n",
       "8          774.71           -63.35         18,027.00               0.90   \n",
       "9          746.39           -54.83         18,027.00               0.91   \n",
       "10         821.36          -174.17         18,027.00               0.94   \n",
       "11         747.89          -153.16         18,027.00               0.96   \n",
       "\n",
       "    balance_kbd   stock_kb  identity_stock_unclipped_kb  \\\n",
       "0        125.51 402,441.45                   408,011.76   \n",
       "1        -16.58 407,121.62                   407,497.70   \n",
       "2        252.12 410,311.82                   415,061.35   \n",
       "3        685.65 412,584.02                   436,316.35   \n",
       "4        252.11 418,198.74                   443,879.74   \n",
       "5         58.33 420,626.06                   445,688.02   \n",
       "6        598.23 422,217.11                   464,233.08   \n",
       "7        873.76 425,118.40                   488,698.47   \n",
       "8        468.21 430,310.96                   503,212.91   \n",
       "9        221.84 431,980.62                   509,868.05   \n",
       "10      -289.58 433,010.61                   500,890.94   \n",
       "11      -386.30 430,462.99                   489,302.00   \n",
       "\n",
       "    model_reconciliation_kbd  \n",
       "0                    -179.69  \n",
       "1                     167.56  \n",
       "2                    -145.78  \n",
       "3                    -612.35  \n",
       "4                     -64.96  \n",
       "5                      19.97  \n",
       "6                    -546.90  \n",
       "7                    -770.15  \n",
       "8                    -300.71  \n",
       "9                    -166.18  \n",
       "10                    322.81  \n",
       "11                    301.38  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the conditional 12-month flow and stock outlook. The statistical stock\n",
    "# path and raw accumulated flow identity need not coincide: reconciliation\n",
    "# measures predicted stock change minus forecast net supply, not an observed flow.\n",
    "\n",
    "outlook=tables['us_forecast_12m']\n",
    "display(outlook[['month','production_kbd','demand_kbd','imports_kbd','exports_kbd','transfers_kbd',\n",
    "    'adjustments_kbd','cdu_capacity_kbd','utilization_ratio','balance_kbd','stock_kb','identity_stock_unclipped_kb','model_reconciliation_kbd']])\n",
    "fig,axes=plt.subplots(2,2,figsize=(14,8))\n",
    "for c,label in [('supply_kbd','Supply including receipts/adjustments/transfers'),('total_demand_kbd','Refinery input + exports + direct use')]:\n",
    "    axes[0,0].plot(outlook.month,outlook[c],label=label)\n",
    "axes[0,0].set(title='U.S. monthly flow outlook',ylabel='Thousand barrels/day');axes[0,0].legend(fontsize=7)\n",
    "axes[0,1].bar(outlook.month,outlook.balance_kbd,width=20)\n",
    "axes[0,1].axhline(0,color='black');axes[0,1].set(title='Projected net flow balance',ylabel='Thousand barrels/day')\n",
    "recent=tables['us_monthly_model'].tail(24)\n",
    "axes[1,0].plot(recent.month,recent.stock_kb/1000,label='Actual')\n",
    "axes[1,0].plot(outlook.month,outlook.stock_kb/1000,label=metadata['year_ahead_model'])\n",
    "axes[1,0].plot(outlook.month,outlook.identity_stock_unclipped_kb/1000,label='Raw flow identity',linestyle='--')\n",
    "axes[1,0].plot(outlook.month,np.repeat(recent.stock_kb.iloc[-1]/1000,12),label='Persistence baseline',linestyle=':')\n",
    "axes[1,0].set(title='Conditional stock paths: no calibrated intervals',ylabel='Million barrels');axes[1,0].legend(fontsize=7)\n",
    "for p,g in tables['padd_forecast_12m'].groupby('padd'):\n",
    "    axes[1,1].plot(g.month,g.stock_kb/1000,label=f'PADD {p}')\n",
    "axes[1,1].set(title='Regional commercial stock paths',ylabel='Million barrels');axes[1,1].legend(fontsize=8)\n",
    "for ax in axes.flat:ax.tick_params(axis='x',rotation=30)\n",
    "plt.tight_layout();plt.show()\n",
    "fig,axes=plt.subplots(1,2,figsize=(14,4))\n",
    "# Convert utilization ratios to percent for display; capacity is plotted\n",
    "# separately to expose the constant-capacity forecast assumption.\n",
    "axes[0].plot(tables['us_monthly_model'].tail(36).month,tables['us_monthly_model'].tail(36).utilization_ratio*100,label='Observed')\n",
    "axes[0].plot(outlook.month,outlook.utilization_ratio*100,label='Forecast')\n",
    "axes[0].set(title='Crude input / CDU capacity',ylabel='Utilization (%)');axes[0].legend()\n",
    "for p,g in tables['padd_forecast_12m'].groupby('padd'):\n",
    "    axes[1].plot(g.month,g.cdu_capacity_kbd,label=f'PADD {p}')\n",
    "axes[1].set(title='Capacity held constant at forecast origin',ylabel='Thousand barrels/day');axes[1].legend()\n",
    "plt.tight_layout();plt.show()\n",
    "# Validate that capacity stays fixed within each PADD's forward path and\n",
    "# that national demand equals aggregate utilization times aggregate capacity.\n",
    "assert tables['padd_forecast_12m'].groupby('padd').cdu_capacity_kbd.nunique().eq(1).all()\n",
    "np.testing.assert_allclose(outlook.demand_kbd,outlook.utilization_ratio*outlook.cdu_capacity_kbd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79f156d7",
   "metadata": {},
   "source": [
    "### Forecast performance\n",
    "\n",
    "The tables below compare 24 one-month forecasts and 13 overlapping twelve-month paths.\n",
    "\n",
    "Positive benchmark improvement means lower MAE, positive forecast residuals indicate overestimated stocks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b9c65f-short-heading",
   "metadata": {},
   "source": [
    "### One-month national results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "30b9c65f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:52.916051Z",
     "iopub.status.busy": "2026-09-16T22:03:52.915924Z",
     "iopub.status.idle": "2026-09-16T22:03:52.980299Z",
     "shell.execute_reply": "2026-09-16T22:03:52.979799Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE (million bbl)</th>\n",
       "      <th>RMSE (million bbl)</th>\n",
       "      <th>R²</th>\n",
       "      <th>Forecast observations</th>\n",
       "      <th>Bias (million bbl)</th>\n",
       "      <th>MAE improvement vs persistence (%)</th>\n",
       "      <th>MAE improvement vs year ago (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>seasonal_change</th>\n",
       "      <td>6.78</td>\n",
       "      <td>9.04</td>\n",
       "      <td>0.44</td>\n",
       "      <td>24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>29.59</td>\n",
       "      <td>38.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>selected_padd_models</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.22</td>\n",
       "      <td>0.42</td>\n",
       "      <td>24</td>\n",
       "      <td>2.28</td>\n",
       "      <td>24.71</td>\n",
       "      <td>34.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_residual_change</th>\n",
       "      <td>7.34</td>\n",
       "      <td>9.25</td>\n",
       "      <td>0.42</td>\n",
       "      <td>24</td>\n",
       "      <td>2.76</td>\n",
       "      <td>23.79</td>\n",
       "      <td>33.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_forest_change</th>\n",
       "      <td>7.86</td>\n",
       "      <td>9.56</td>\n",
       "      <td>0.38</td>\n",
       "      <td>24</td>\n",
       "      <td>3.66</td>\n",
       "      <td>18.35</td>\n",
       "      <td>28.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spline_ridge_change</th>\n",
       "      <td>8.34</td>\n",
       "      <td>10.24</td>\n",
       "      <td>0.29</td>\n",
       "      <td>24</td>\n",
       "      <td>3.22</td>\n",
       "      <td>13.44</td>\n",
       "      <td>24.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensemble_change</th>\n",
       "      <td>8.57</td>\n",
       "      <td>10.29</td>\n",
       "      <td>0.28</td>\n",
       "      <td>24</td>\n",
       "      <td>3.21</td>\n",
       "      <td>10.97</td>\n",
       "      <td>21.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost_change</th>\n",
       "      <td>8.90</td>\n",
       "      <td>11.14</td>\n",
       "      <td>0.15</td>\n",
       "      <td>24</td>\n",
       "      <td>5.58</td>\n",
       "      <td>7.62</td>\n",
       "      <td>19.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neural_network_change</th>\n",
       "      <td>9.01</td>\n",
       "      <td>10.53</td>\n",
       "      <td>0.24</td>\n",
       "      <td>24</td>\n",
       "      <td>4.12</td>\n",
       "      <td>6.43</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_ridge_change</th>\n",
       "      <td>9.23</td>\n",
       "      <td>11.27</td>\n",
       "      <td>0.14</td>\n",
       "      <td>24</td>\n",
       "      <td>2.80</td>\n",
       "      <td>4.21</td>\n",
       "      <td>16.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>damped_ridge_change</th>\n",
       "      <td>9.44</td>\n",
       "      <td>11.55</td>\n",
       "      <td>0.09</td>\n",
       "      <td>24</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.01</td>\n",
       "      <td>14.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>polynomial_ridge_change</th>\n",
       "      <td>9.51</td>\n",
       "      <td>11.41</td>\n",
       "      <td>0.11</td>\n",
       "      <td>24</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1.26</td>\n",
       "      <td>13.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>persistence</th>\n",
       "      <td>9.63</td>\n",
       "      <td>12.19</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>24</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rolling_ridge_change</th>\n",
       "      <td>9.95</td>\n",
       "      <td>12.38</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>24</td>\n",
       "      <td>5.40</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>9.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huber_change</th>\n",
       "      <td>10.02</td>\n",
       "      <td>11.61</td>\n",
       "      <td>0.08</td>\n",
       "      <td>24</td>\n",
       "      <td>5.29</td>\n",
       "      <td>-3.99</td>\n",
       "      <td>8.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge_change</th>\n",
       "      <td>10.05</td>\n",
       "      <td>12.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>24</td>\n",
       "      <td>2.92</td>\n",
       "      <td>-4.31</td>\n",
       "      <td>8.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>constrained_level</th>\n",
       "      <td>10.76</td>\n",
       "      <td>12.13</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>24</td>\n",
       "      <td>5.85</td>\n",
       "      <td>-11.68</td>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_naive</th>\n",
       "      <td>10.99</td>\n",
       "      <td>13.92</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>24</td>\n",
       "      <td>7.55</td>\n",
       "      <td>-14.12</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forecast_flow_identity</th>\n",
       "      <td>11.88</td>\n",
       "      <td>14.27</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-23.34</td>\n",
       "      <td>-8.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          MAE (million bbl)  RMSE (million bbl)    R²  \\\n",
       "model                                                                   \n",
       "seasonal_change                        6.78                9.04  0.44   \n",
       "selected_padd_models                   7.25                9.22  0.42   \n",
       "seasonal_residual_change               7.34                9.25  0.42   \n",
       "random_forest_change                   7.86                9.56  0.38   \n",
       "spline_ridge_change                    8.34               10.24  0.29   \n",
       "ensemble_change                        8.57               10.29  0.28   \n",
       "xgboost_change                         8.90               11.14  0.15   \n",
       "neural_network_change                  9.01               10.53  0.24   \n",
       "seasonal_ridge_change                  9.23               11.27  0.14   \n",
       "damped_ridge_change                    9.44               11.55  0.09   \n",
       "polynomial_ridge_change                9.51               11.41  0.11   \n",
       "persistence                            9.63               12.19 -0.01   \n",
       "rolling_ridge_change                   9.95               12.38 -0.04   \n",
       "huber_change                          10.02               11.61  0.08   \n",
       "ridge_change                          10.05               12.10  0.00   \n",
       "constrained_level                     10.76               12.13 -0.00   \n",
       "seasonal_naive                        10.99               13.92 -0.32   \n",
       "forecast_flow_identity                11.88               14.27 -0.39   \n",
       "\n",
       "                          Forecast observations  Bias (million bbl)  \\\n",
       "model                                                                 \n",
       "seasonal_change                              24                0.81   \n",
       "selected_padd_models                         24                2.28   \n",
       "seasonal_residual_change                     24                2.76   \n",
       "random_forest_change                         24                3.66   \n",
       "spline_ridge_change                          24                3.22   \n",
       "ensemble_change                              24                3.21   \n",
       "xgboost_change                               24                5.58   \n",
       "neural_network_change                        24                4.12   \n",
       "seasonal_ridge_change                        24                2.80   \n",
       "damped_ridge_change                          24                2.51   \n",
       "polynomial_ridge_change                      24                2.85   \n",
       "persistence                                  24                1.52   \n",
       "rolling_ridge_change                         24                5.40   \n",
       "huber_change                                 24                5.29   \n",
       "ridge_change                                 24                2.92   \n",
       "constrained_level                            24                5.85   \n",
       "seasonal_naive                               24                7.55   \n",
       "forecast_flow_identity                       24               -0.18   \n",
       "\n",
       "                          MAE improvement vs persistence (%)  \\\n",
       "model                                                          \n",
       "seasonal_change                                        29.59   \n",
       "selected_padd_models                                   24.71   \n",
       "seasonal_residual_change                               23.79   \n",
       "random_forest_change                                   18.35   \n",
       "spline_ridge_change                                    13.44   \n",
       "ensemble_change                                        10.97   \n",
       "xgboost_change                                          7.62   \n",
       "neural_network_change                                   6.43   \n",
       "seasonal_ridge_change                                   4.21   \n",
       "damped_ridge_change                                     2.01   \n",
       "polynomial_ridge_change                                 1.26   \n",
       "persistence                                             0.00   \n",
       "rolling_ridge_change                                   -3.34   \n",
       "huber_change                                           -3.99   \n",
       "ridge_change                                           -4.31   \n",
       "constrained_level                                     -11.68   \n",
       "seasonal_naive                                        -14.12   \n",
       "forecast_flow_identity                                -23.34   \n",
       "\n",
       "                          MAE improvement vs year ago (%)  \n",
       "model                                                      \n",
       "seasonal_change                                     38.30  \n",
       "selected_padd_models                                34.03  \n",
       "seasonal_residual_change                            33.21  \n",
       "random_forest_change                                28.45  \n",
       "spline_ridge_change                                 24.15  \n",
       "ensemble_change                                     21.98  \n",
       "xgboost_change                                      19.05  \n",
       "neural_network_change                               18.00  \n",
       "seasonal_ridge_change                               16.06  \n",
       "damped_ridge_change                                 14.14  \n",
       "polynomial_ridge_change                             13.47  \n",
       "persistence                                         12.37  \n",
       "rolling_ridge_change                                 9.44  \n",
       "huber_change                                         8.87  \n",
       "ridge_change                                         8.60  \n",
       "constrained_level                                    2.13  \n",
       "seasonal_naive                                       0.00  \n",
       "forecast_flow_identity                              -8.08  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summarize national forecasts on matched months and origins.\n",
    "one = tables['us_predictions'].query(\"split == 'holdout'\").copy()\n",
    "one_name = metadata.get('one_month_model', 'selected_padd_models')\n",
    "long_name = metadata['year_ahead_model']\n",
    "long = tables['us_recursive_holdout_predictions'].copy()\n",
    "history_stocks = tables['us_monthly_model'].set_index('month').stock_kb\n",
    "base = long[long.model.eq(long_name)].copy()\n",
    "assert not base.duplicated(['origin_month', 'month']).any()\n",
    "year_ago = base.copy()\n",
    "lag_dates = year_ago.month - pd.DateOffset(years=1)\n",
    "assert (lag_dates <= year_ago.origin_month).all()\n",
    "year_ago['predicted_kb'] = history_stocks.reindex(lag_dates).to_numpy()\n",
    "assert year_ago.predicted_kb.notna().all()\n",
    "year_ago['model'] = 'seasonal_naive'\n",
    "long = pd.concat([long[~long.model.eq('seasonal_naive')], year_ago], ignore_index=True)\n",
    "\n",
    "def summary_table(frame):\n",
    "    rows = []\n",
    "    reference = frame[frame.model.eq('persistence')].set_index(['origin_month', 'month'])\n",
    "    for name, group in frame.groupby('model'):\n",
    "        aligned = group.set_index(['origin_month', 'month']).sort_index()\n",
    "        pd.testing.assert_index_equal(aligned.index, reference.sort_index().index)\n",
    "        np.testing.assert_allclose(aligned.actual_kb, reference.sort_index().actual_kb)\n",
    "        rows.append(dict(model=name, **score(group.actual_kb, group.predicted_kb),\n",
    "                         bias_kb=(group.predicted_kb-group.actual_kb).mean()))\n",
    "    result = pd.DataFrame(rows).set_index('model').sort_values('mae_kb')\n",
    "    for benchmark, label in [('persistence','MAE improvement vs persistence (%)'),\n",
    "                             ('seasonal_naive','MAE improvement vs year ago (%)')]:\n",
    "        result[label] = 100*(1-result.mae_kb/result.loc[benchmark,'mae_kb'])\n",
    "    return result\n",
    "\n",
    "short_scores, long_scores = summary_table(one), summary_table(long)\n",
    "report = short_scores.copy()\n",
    "for column in ['mae_kb','rmse_kb','bias_kb']:\n",
    "    report[column] /= 1000\n",
    "display(report.rename(columns={'mae_kb':'MAE (million bbl)', 'rmse_kb':'RMSE (million bbl)',\n",
    "    'bias_kb':'Bias (million bbl)', 'r2':'R²', 'n':'Forecast observations'}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b9c65f-long-heading",
   "metadata": {},
   "source": [
    "### Twelve-month path results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "30b9c65f-long-table",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:52.981505Z",
     "iopub.status.busy": "2026-09-16T22:03:52.981393Z",
     "iopub.status.idle": "2026-09-16T22:03:52.987189Z",
     "shell.execute_reply": "2026-09-16T22:03:52.986892Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE (million bbl)</th>\n",
       "      <th>RMSE (million bbl)</th>\n",
       "      <th>R²</th>\n",
       "      <th>Forecast observations</th>\n",
       "      <th>Bias (million bbl)</th>\n",
       "      <th>MAE improvement vs persistence (%)</th>\n",
       "      <th>MAE improvement vs year ago (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>constrained_level</th>\n",
       "      <td>9.06</td>\n",
       "      <td>11.06</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>156</td>\n",
       "      <td>1.53</td>\n",
       "      <td>17.32</td>\n",
       "      <td>25.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>persistence</th>\n",
       "      <td>10.95</td>\n",
       "      <td>13.47</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>156</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_change</th>\n",
       "      <td>11.31</td>\n",
       "      <td>13.85</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>156</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seasonal_naive</th>\n",
       "      <td>12.14</td>\n",
       "      <td>15.39</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>156</td>\n",
       "      <td>10.15</td>\n",
       "      <td>-10.84</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   MAE (million bbl)  RMSE (million bbl)    R²  \\\n",
       "model                                                            \n",
       "constrained_level               9.06               11.06 -0.08   \n",
       "persistence                    10.95               13.47 -0.60   \n",
       "seasonal_change                11.31               13.85 -0.69   \n",
       "seasonal_naive                 12.14               15.39 -1.08   \n",
       "\n",
       "                   Forecast observations  Bias (million bbl)  \\\n",
       "model                                                          \n",
       "constrained_level                    156                1.53   \n",
       "persistence                          156                2.30   \n",
       "seasonal_change                      156               -1.88   \n",
       "seasonal_naive                       156               10.15   \n",
       "\n",
       "                   MAE improvement vs persistence (%)  \\\n",
       "model                                                   \n",
       "constrained_level                               17.32   \n",
       "persistence                                      0.00   \n",
       "seasonal_change                                 -3.29   \n",
       "seasonal_naive                                 -10.84   \n",
       "\n",
       "                   MAE improvement vs year ago (%)  \n",
       "model                                               \n",
       "constrained_level                            25.41  \n",
       "persistence                                   9.78  \n",
       "seasonal_change                               6.81  \n",
       "seasonal_naive                                0.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report = long_scores.copy()\n",
    "for column in ['mae_kb','rmse_kb','bias_kb']:\n",
    "    report[column] /= 1000\n",
    "display(report.rename(columns={'mae_kb':'MAE (million bbl)', 'rmse_kb':'RMSE (million bbl)',\n",
    "    'bias_kb':'Bias (million bbl)', 'r2':'R²', 'n':'Forecast observations'}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b9c65f-score-reading",
   "metadata": {},
   "source": [
    "The lowest observed one-month MAE belongs to `seasonal_change` at **6.78 million barrels**.\n",
    "\n",
    "**Selected one-month method: `seasonal_change`.** MAE is **6.78 million barrels**, RMSE is **9.04 million barrels**, and R² is **0.44**. MAE improvement is **29.6%** versus persistence and **38.3%** versus the same month last year.\n",
    "\n",
    "`constrained_level` was selected using the lowest national MAE across six development-year paths.\n",
    "\n",
    "**Selected twelve-month method: `constrained_level`.** MAE is **9.06 million barrels**, RMSE is **11.06 million barrels**, and R² is **-0.08**. MAE improvement is **17.3%** versus persistence and **25.4%** versus the same month last year. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "30b9c65f-error-plots",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:52.988158Z",
     "iopub.status.busy": "2026-09-16T22:03:52.988036Z",
     "iopub.status.idle": "2026-09-16T22:03:53.214309Z",
     "shell.execute_reply": "2026-09-16T22:03:53.213701Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected = one[one.model.eq(one_name)].sort_values('month').copy()\n",
    "selected['error'] = (selected.predicted_kb-selected.actual_kb)/1000\n",
    "fig, axes = plt.subplots(1,3,figsize=(16,4))\n",
    "axes[0].bar(selected.month,selected.error,width=20)\n",
    "axes[0].set(title='One-month inventory forecast errors',ylabel='Forecast − actual (million bbl)')\n",
    "axes[1].hist(selected.error,bins=9,edgecolor='white')\n",
    "axes[1].set(title='Distribution of forecast errors',xlabel='Forecast − actual (million bbl)',ylabel='Months')\n",
    "axes[2].scatter(selected.predicted_kb/1000,selected.error)\n",
    "axes[2].set(title='Errors versus forecast stock level',xlabel='Forecast stocks (million bbl)',ylabel='Forecast − actual (million bbl)')\n",
    "for ax in [axes[0],axes[2]]: ax.axhline(0,color='black',linewidth=.8)\n",
    "axes[1].axvline(0,color='black',linewidth=.8)\n",
    "fig.autofmt_xdate(); plt.tight_layout(); plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b9c65f-error-reading",
   "metadata": {},
   "source": [
    "The selected one-month method has average signed error of **+0.81 million barrels**. Its largest absolute monthly miss is **19.50 million barrels**. Positive residuals mean stocks were overestimated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "30b9c65f-horizon-plot",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:53.215653Z",
     "iopub.status.busy": "2026-09-16T22:03:53.215520Z",
     "iopub.status.idle": "2026-09-16T22:03:53.310947Z",
     "shell.execute_reply": "2026-09-16T22:03:53.310414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(11,4))\n",
    "for name, group in long.groupby('model'):\n",
    "    errors = group.assign(error=(group.predicted_kb-group.actual_kb).abs()/1000)\n",
    "    errors.groupby('horizon').error.mean().plot(ax=ax,marker='o',label=name)\n",
    "ax.set(title='Inventory error by forecast horizon',xlabel='Months ahead',ylabel='MAE (million bbl)')\n",
    "ax.legend(fontsize=8); plt.tight_layout(); plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b9c65f-benchmark-reading",
   "metadata": {},
   "source": [
    "**Year-ahead benchmark check:** `constrained_level` has **25.4% lower MAE** than the same month last year."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc391c3f",
   "metadata": {},
   "source": [
    "### Interpretation\n",
    "\n",
    "Higher refinery inputs (demand) reduce the implied inventory build, while higher production or imports increase it (supply).\n",
    "\n",
    "Seasonal changes outperform added model complexity in the national one-month test.\n",
    "\n",
    "The outlook reflects historical relationships and excludes explicit price, outage, and turnaround scenarios."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "041b45ec",
   "metadata": {},
   "source": [
    "### Reproduce the results\n",
    "\n",
    "Run All repeats training and evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fa88c0b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:53.312268Z",
     "iopub.status.busy": "2026-09-16T22:03:53.312147Z",
     "iopub.status.idle": "2026-09-16T22:03:53.675028Z",
     "shell.execute_reply": "2026-09-16T22:03:53.674281Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source integrity, stock scope, target-month isolation, and aggregation checks passed.\n"
     ]
    }
   ],
   "source": [
    "# Check the model's stock scope and accounting identities, then perturb the\n",
    "# last target observation to verify target-month isolation. Finally require\n",
    "# the national forecast to equal the sum of the five regional forecasts.\n",
    "\n",
    "assert not any('spr' in c.lower() for c in panel.columns)\n",
    "np.testing.assert_allclose(panel.demand_kbd, panel.utilization_ratio * panel.cdu_capacity_kbd)\n",
    "check_history=panel[panel.padd.eq(3)].iloc[:40].copy()\n",
    "before=supervised(check_history)\n",
    "# Perturb the final target month's stocks and flows substantially to test that\n",
    "# its own inputs cannot see target-month information.\n",
    "check_history.loc[check_history.index[-1],FLOWS+['stock_kb']]+=100000\n",
    "changed=supervised(check_history)\n",
    "# Only target labels may change after perturbing the final observation. All\n",
    "# predictors, origin dates, and flow-identity forecasts must remain identical.\n",
    "feature_columns=[c for c in before if c not in ['actual_kb','delta_kb']]\n",
    "pd.testing.assert_frame_equal(before[feature_columns],changed[feature_columns])\n",
    "np.testing.assert_allclose(tables['us_forecast_12m'].stock_kb,\n",
    "    tables['padd_forecast_12m'].groupby('month').stock_kb.sum())\n",
    "print('Source integrity, stock scope, target-month isolation, and aggregation checks passed.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "eeecc022",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-16T22:03:53.676512Z",
     "iopub.status.busy": "2026-09-16T22:03:53.676363Z",
     "iopub.status.idle": "2026-09-16T22:03:53.684558Z",
     "shell.execute_reply": "2026-09-16T22:03:53.684099Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product                  Commercial crude oil; excludes strategic reserves\n",
       "covid_exclusion          {'start': '2020-03-01', 'end': '2021-03-01', '...\n",
       "stock_units                                  thousand barrels at month end\n",
       "flow_units                                        thousand barrels per day\n",
       "equation                 S[t] = S[t-1] + days[t] * (field production[t]...\n",
       "data_start                                                      2007-01-01\n",
       "data_end                                                        2026-06-01\n",
       "cv                       10 expanding folds, 12 eligible test months ea...\n",
       "holdout_start                                                   2024-07-01\n",
       "forecast_timing          One month after latest observed EIA month; con...\n",
       "selection                GridSearchCV per PADD/model on development dat...\n",
       "hyperparameter_search    {'method': 'GridSearchCV', 'folds': 10, 'scori...\n",
       "one_month_model                                            seasonal_change\n",
       "year_ahead_model                                         constrained_level\n",
       "horizon_selection        Lowest national MAE across six disjoint 12-mon...\n",
       "flow_forecast            Last 60 non-COVID observations, daily rates, l...\n",
       "forecast_stock_floor                                                     0\n",
       "uncertainty              Point forecasts only; no calibrated prediction...\n",
       "recursive_validation     13 overlapping 12-month paths entirely in fina...\n",
       "versions                 {'python': '3.13.9', 'numpy': '2.3.5', 'pandas...\n",
       "sources                  [https://www.eia.gov/dnav/pet/pet_sum_snd_d_r1...\n",
       "Name: Run configuration, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notebook checks passed. Saved result files:\n",
      "all_fitted_models.joblib\n",
      "best_parameters.csv\n",
      "candidate_addition_comparison.csv\n",
      "candidate_models.json\n",
      "constrained_model_coefficients.csv\n",
      "fit_warnings.csv\n",
      "fitted_horizon_models.joblib\n",
      "fitted_models.joblib\n",
      "fold_metrics.csv\n",
      "grid_search_results.csv\n",
      "latest_forecast.csv\n",
      "model_metadata.json\n",
      "national_model_cv_metrics.csv\n",
      "padd_forecast_12m.csv\n",
      "padd_model_coefficients.csv\n",
      "padd_model_metrics.csv\n",
      "padd_monthly_model.csv\n",
      "padd_predictions.csv\n",
      "recursive_cv_predictions.csv\n",
      "recursive_holdout_metrics.csv\n",
      "recursive_holdout_predictions.csv\n",
      "refit_comparison.csv\n",
      "regional_cv_winners.csv\n",
      "regional_error_review.csv\n",
      "selected_models.csv\n",
      "training_sample_audit.csv\n",
      "us_forecast_12m.csv\n",
      "us_model_metrics.csv\n",
      "us_monthly_model.csv\n",
      "us_predictions.csv\n",
      "us_recursive_cv_predictions.csv\n",
      "us_recursive_holdout_predictions.csv\n",
      "us_recursive_horizon_metrics.csv\n",
      "year_ahead_model_cv_metrics.csv\n",
      "year_ahead_refit_comparison.csv\n"
     ]
    }
   ],
   "source": [
    "# Inspect run configuration and saved artifacts, and validate output shape: no\n",
    "# duplicate forecasts, all five PADDs each month, and a full 12-month outlook.\n",
    "\n",
    "display(pd.Series(metadata,name='Run configuration'))\n",
    "assert not tables['padd_predictions'].duplicated(['padd','month','model','split']).any()\n",
    "assert tables['padd_forecast_12m'].groupby('month').padd.nunique().eq(5).all()\n",
    "assert len(outlook)==12\n",
    "assert len(tables['latest_forecast'])==6\n",
    "assert not ((tables['us_recursive_cv_predictions'].month>=pd.Timestamp(metadata['holdout_start']))).any()\n",
    "print('Notebook checks passed. Saved result files:')\n",
    "for p in sorted(OUTPUT.iterdir()):print(p.name)"
   ]
  }
 ],
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