{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "83dd52ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0f6f961",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>tanggal</th>\n",
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       "      <td>2024-01-03</td>\n",
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       "<p>609 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "       tanggal  jumlah_penumpang_per_hari\n",
       "0   2024-01-01                       3166\n",
       "1   2024-01-02                       2901\n",
       "2   2024-01-03                       2795\n",
       "3   2024-01-04                       2784\n",
       "4   2024-01-05                       3219\n",
       "..         ...                        ...\n",
       "604 2025-08-27                       3702\n",
       "605 2025-08-28                       3412\n",
       "606 2025-08-29                       3201\n",
       "607 2025-08-30                       2406\n",
       "608 2025-08-31                       2091\n",
       "\n",
       "[609 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('lrt_daily.csv', sep=';', decimal=',')\n",
    "df['tanggal'] = pd.to_datetime(df['tanggal'], format='%Y-%m-%d')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "17565efd",
   "metadata": {},
   "outputs": [
    {
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       "<p>609 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "       tanggal  is_weekend  is_holiday_nat  flag_contiguous_off  \\\n",
       "0   2024-01-01           0               1                    0   \n",
       "1   2024-01-02           0               0                    0   \n",
       "2   2024-01-03           0               0                    0   \n",
       "3   2024-01-04           0               0                    0   \n",
       "4   2024-01-05           0               0                    0   \n",
       "..         ...         ...             ...                  ...   \n",
       "604 2025-08-27           0               0                    0   \n",
       "605 2025-08-28           0               0                    0   \n",
       "606 2025-08-29           0               0                    0   \n",
       "607 2025-08-30           1               0                    0   \n",
       "608 2025-08-31           1               0                    0   \n",
       "\n",
       "     flag_almost_contiguous_off  flag_almost_long_contiguous_off  \n",
       "0                             0                                0  \n",
       "1                             0                                0  \n",
       "2                             0                                0  \n",
       "3                             0                                0  \n",
       "4                             0                                0  \n",
       "..                          ...                              ...  \n",
       "604                           0                                0  \n",
       "605                           0                                0  \n",
       "606                           0                                0  \n",
       "607                           0                                0  \n",
       "608                           0                                0  \n",
       "\n",
       "[609 rows x 6 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_flags = pd.read_csv('lrt_offday_flags.csv')\n",
    "df_flags['tanggal'] = pd.to_datetime(df_flags['tanggal'], format='%Y-%m-%d')\n",
    "df_flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a92a5bda",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       tanggal  jumlah_penumpang_per_hari  is_weekend  is_holiday_nat  \\\n",
       "0   2024-01-01                       3166           0               1   \n",
       "1   2024-01-02                       2901           0               0   \n",
       "2   2024-01-03                       2795           0               0   \n",
       "3   2024-01-04                       2784           0               0   \n",
       "4   2024-01-05                       3219           0               0   \n",
       "..         ...                        ...         ...             ...   \n",
       "604 2025-08-27                       3702           0               0   \n",
       "605 2025-08-28                       3412           0               0   \n",
       "606 2025-08-29                       3201           0               0   \n",
       "607 2025-08-30                       2406           1               0   \n",
       "608 2025-08-31                       2091           1               0   \n",
       "\n",
       "     flag_contiguous_off  flag_almost_contiguous_off  \\\n",
       "0                      0                           0   \n",
       "1                      0                           0   \n",
       "2                      0                           0   \n",
       "3                      0                           0   \n",
       "4                      0                           0   \n",
       "..                   ...                         ...   \n",
       "604                    0                           0   \n",
       "605                    0                           0   \n",
       "606                    0                           0   \n",
       "607                    0                           0   \n",
       "608                    0                           0   \n",
       "\n",
       "     flag_almost_long_contiguous_off  \n",
       "0                                  0  \n",
       "1                                  0  \n",
       "2                                  0  \n",
       "3                                  0  \n",
       "4                                  0  \n",
       "..                               ...  \n",
       "604                                0  \n",
       "605                                0  \n",
       "606                                0  \n",
       "607                                0  \n",
       "608                                0  \n",
       "\n",
       "[609 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged = df.merge(df_flags, on='tanggal', how='left')\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e8845d6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2025-08-29</td>\n",
       "      <td>3201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>2025-08-30</td>\n",
       "      <td>2406</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>2025-08-31</td>\n",
       "      <td>2091</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>609 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tanggal  jumlah_penumpang_per_hari  is_weekend  is_holiday_nat  \\\n",
       "0   2024-01-01                       3166           0               1   \n",
       "1   2024-01-02                       2901           0               0   \n",
       "2   2024-01-03                       2795           0               0   \n",
       "3   2024-01-04                       2784           0               0   \n",
       "4   2024-01-05                       3219           0               0   \n",
       "..         ...                        ...         ...             ...   \n",
       "604 2025-08-27                       3702           0               0   \n",
       "605 2025-08-28                       3412           0               0   \n",
       "606 2025-08-29                       3201           0               0   \n",
       "607 2025-08-30                       2406           1               0   \n",
       "608 2025-08-31                       2091           1               0   \n",
       "\n",
       "     flag_contiguous_off  flag_almost_contiguous_off  \n",
       "0                      0                           0  \n",
       "1                      0                           0  \n",
       "2                      0                           0  \n",
       "3                      0                           0  \n",
       "4                      0                           0  \n",
       "..                   ...                         ...  \n",
       "604                    0                           0  \n",
       "605                    0                           0  \n",
       "606                    0                           0  \n",
       "607                    0                           0  \n",
       "608                    0                           0  \n",
       "\n",
       "[609 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged.drop(columns=['flag_almost_long_contiguous_off'], inplace=True)\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f430b2ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "events\n",
      "0    604\n",
      "1      5\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Flag specified event dates as 1 in a new 'events' column; others = 0\n",
    "import pandas as pd\n",
    "\n",
    "event_dates = pd.to_datetime([\n",
    "    \"2025-04-24\",  # Promo Hari Kartini\n",
    "    \"2024-06-22\",  # Promo HUT Jakarta\n",
    "    \"2025-06-22\",  # Promo HUT Jakarta\n",
    "    \"2024-06-23\",  # Promo HUT Jakarta (masih)\n",
    "    \"2025-07-01\"  # Promo Hari Bhayangkara\n",
    "])\n",
    "\n",
    "df_merged = df_merged.copy()\n",
    "\n",
    "# Use 'tanggal' column if present, else use the index\n",
    "if \"tanggal\" in df_merged.columns:\n",
    "    dates_series = pd.to_datetime(df_merged[\"tanggal\"]).dt.normalize()\n",
    "else:\n",
    "    dates_series = pd.to_datetime(df_merged.index).tz_localize(None).normalize()\n",
    "\n",
    "df_merged[\"events\"] = dates_series.isin(event_dates.normalize()).astype(int)\n",
    "\n",
    "# (optional) quick check\n",
    "print(df_merged[\"events\"].value_counts(dropna=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "65f17d8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>tanggal</th>\n",
       "      <th>jumlah_penumpang_per_hari</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>is_holiday_nat</th>\n",
       "      <th>flag_contiguous_off</th>\n",
       "      <th>flag_almost_contiguous_off</th>\n",
       "      <th>events</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>3166</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>2901</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-03</td>\n",
       "      <td>2795</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-01-04</td>\n",
       "      <td>2784</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-01-05</td>\n",
       "      <td>3219</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tanggal  jumlah_penumpang_per_hari  is_weekend  is_holiday_nat  \\\n",
       "0 2024-01-01                       3166           0               1   \n",
       "1 2024-01-02                       2901           0               0   \n",
       "2 2024-01-03                       2795           0               0   \n",
       "3 2024-01-04                       2784           0               0   \n",
       "4 2024-01-05                       3219           0               0   \n",
       "\n",
       "   flag_contiguous_off  flag_almost_contiguous_off  events  \n",
       "0                    0                           0       0  \n",
       "1                    0                           0       0  \n",
       "2                    0                           0       0  \n",
       "3                    0                           0       0  \n",
       "4                    0                           0       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>tanggal</th>\n",
       "      <th>jumlah_penumpang_per_hari</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>is_holiday_nat</th>\n",
       "      <th>flag_contiguous_off</th>\n",
       "      <th>flag_almost_contiguous_off</th>\n",
       "      <th>events</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>2025-08-27</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>2025-08-30</td>\n",
       "      <td>2406</td>\n",
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       "      <td>0</td>\n",
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       "      <th>608</th>\n",
       "      <td>2025-08-31</td>\n",
       "      <td>2091</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tanggal  jumlah_penumpang_per_hari  is_weekend  is_holiday_nat  \\\n",
       "604 2025-08-27                       3702           0               0   \n",
       "605 2025-08-28                       3412           0               0   \n",
       "606 2025-08-29                       3201           0               0   \n",
       "607 2025-08-30                       2406           1               0   \n",
       "608 2025-08-31                       2091           1               0   \n",
       "\n",
       "     flag_contiguous_off  flag_almost_contiguous_off  events  \n",
       "604                    0                           0       0  \n",
       "605                    0                           0       0  \n",
       "606                    0                           0       0  \n",
       "607                    0                           0       0  \n",
       "608                    0                           0       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train = df_merged.iloc[:-56]\n",
    "df_test = df_merged.iloc[-56:]\n",
    "display(df_train.head())\n",
    "display(df_test.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78fe4d1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>jumlah_penumpang_per_hari</td> <th>  R-squared:         </th> <td>   0.340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                       <td>OLS</td>            <th>  Adj. R-squared:    </th> <td>   0.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>Least Squares</td>       <th>  F-statistic:       </th> <td>   56.31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                 <td>Tue, 02 Dec 2025</td>      <th>  Prob (F-statistic):</th> <td>3.30e-47</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                     <td>16:33:02</td>          <th>  Log-Likelihood:    </th> <td> -4328.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>          <td>   553</td>           <th>  AIC:               </th> <td>   8668.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>              <td>   547</td>           <th>  BIC:               </th> <td>   8694.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                  <td>     5</td>           <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>          <td>nonrobust</td>         <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "               <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                      <td> 3287.3413</td> <td>   31.498</td> <td>  104.367</td> <td> 0.000</td> <td> 3225.470</td> <td> 3349.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>is_weekend</th>                 <td>  -61.7593</td> <td>   60.237</td> <td>   -1.025</td> <td> 0.306</td> <td> -180.083</td> <td>   56.564</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>is_holiday_nat</th>             <td>  196.4907</td> <td>  131.098</td> <td>    1.499</td> <td> 0.135</td> <td>  -61.026</td> <td>  454.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>flag_contiguous_off</th>        <td> -525.1765</td> <td>  127.650</td> <td>   -4.114</td> <td> 0.000</td> <td> -775.922</td> <td> -274.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>flag_almost_contiguous_off</th> <td> -271.2648</td> <td>  143.174</td> <td>   -1.895</td> <td> 0.059</td> <td> -552.504</td> <td>    9.974</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>events</th>                     <td> 4378.1142</td> <td>  274.844</td> <td>   15.929</td> <td> 0.000</td> <td> 3838.236</td> <td> 4917.992</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>199.834</td> <th>  Durbin-Watson:     </th> <td>   0.853</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 858.029</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.591</td>  <th>  Prob(JB):          </th> <td>4.80e-187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 8.207</td>  <th>  Cond. No.          </th> <td>    11.2</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}                & jumlah\\_penumpang\\_per\\_hari & \\textbf{  R-squared:         } &     0.340   \\\\\n",
       "\\textbf{Model:}                        &             OLS              & \\textbf{  Adj. R-squared:    } &     0.334   \\\\\n",
       "\\textbf{Method:}                       &        Least Squares         & \\textbf{  F-statistic:       } &     56.31   \\\\\n",
       "\\textbf{Date:}                         &       Tue, 02 Dec 2025       & \\textbf{  Prob (F-statistic):} &  3.30e-47   \\\\\n",
       "\\textbf{Time:}                         &           16:33:02           & \\textbf{  Log-Likelihood:    } &   -4328.1   \\\\\n",
       "\\textbf{No. Observations:}             &               553            & \\textbf{  AIC:               } &     8668.   \\\\\n",
       "\\textbf{Df Residuals:}                 &               547            & \\textbf{  BIC:               } &     8694.   \\\\\n",
       "\\textbf{Df Model:}                     &                 5            & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}              &          nonrobust           & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                       & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}                         &    3287.3413  &       31.498     &   104.367  &         0.000        &     3225.470    &     3349.213     \\\\\n",
       "\\textbf{is\\_weekend}                   &     -61.7593  &       60.237     &    -1.025  &         0.306        &     -180.083    &       56.564     \\\\\n",
       "\\textbf{is\\_holiday\\_nat}              &     196.4907  &      131.098     &     1.499  &         0.135        &      -61.026    &      454.008     \\\\\n",
       "\\textbf{flag\\_contiguous\\_off}         &    -525.1765  &      127.650     &    -4.114  &         0.000        &     -775.922    &     -274.431     \\\\\n",
       "\\textbf{flag\\_almost\\_contiguous\\_off} &    -271.2648  &      143.174     &    -1.895  &         0.059        &     -552.504    &        9.974     \\\\\n",
       "\\textbf{events}                        &    4378.1142  &      274.844     &    15.929  &         0.000        &     3838.236    &     4917.992     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 199.834 & \\textbf{  Durbin-Watson:     } &     0.853  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &   858.029  \\\\\n",
       "\\textbf{Skew:}          &   1.591 & \\textbf{  Prob(JB):          } & 4.80e-187  \\\\\n",
       "\\textbf{Kurtosis:}      &   8.207 & \\textbf{  Cond. No.          } &      11.2  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                OLS Regression Results                               \n",
       "=====================================================================================\n",
       "Dep. Variable:     jumlah_penumpang_per_hari   R-squared:                       0.340\n",
       "Model:                                   OLS   Adj. R-squared:                  0.334\n",
       "Method:                        Least Squares   F-statistic:                     56.31\n",
       "Date:                       Tue, 02 Dec 2025   Prob (F-statistic):           3.30e-47\n",
       "Time:                               16:33:02   Log-Likelihood:                -4328.1\n",
       "No. Observations:                        553   AIC:                             8668.\n",
       "Df Residuals:                            547   BIC:                             8694.\n",
       "Df Model:                                  5                                         \n",
       "Covariance Type:                   nonrobust                                         \n",
       "==============================================================================================\n",
       "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------------\n",
       "const                       3287.3413     31.498    104.367      0.000    3225.470    3349.213\n",
       "is_weekend                   -61.7593     60.237     -1.025      0.306    -180.083      56.564\n",
       "is_holiday_nat               196.4907    131.098      1.499      0.135     -61.026     454.008\n",
       "flag_contiguous_off         -525.1765    127.650     -4.114      0.000    -775.922    -274.431\n",
       "flag_almost_contiguous_off  -271.2648    143.174     -1.895      0.059    -552.504       9.974\n",
       "events                      4378.1142    274.844     15.929      0.000    3838.236    4917.992\n",
       "==============================================================================\n",
       "Omnibus:                      199.834   Durbin-Watson:                   0.853\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              858.029\n",
       "Skew:                           1.591   Prob(JB):                    4.80e-187\n",
       "Kurtosis:                       8.207   Cond. No.                         11.2\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "y = df_train['jumlah_penumpang_per_hari']\n",
    "X = df_train.drop(columns=['tanggal', 'jumlah_penumpang_per_hari'])\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model_ols = sm.OLS(y, X).fit()\n",
    "model_ols.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8ec89faa",
   "metadata": {
    "vscode": {
     "languageId": "javascript"
    }
   },
   "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>tanggal</th>\n",
       "      <th>jumlah_penumpang_per_hari</th>\n",
       "      <th>residuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>3166</td>\n",
       "      <td>-317.832049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>2901</td>\n",
       "      <td>-386.341337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-03</td>\n",
       "      <td>2795</td>\n",
       "      <td>-492.341337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-01-04</td>\n",
       "      <td>2784</td>\n",
       "      <td>-503.341337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-01-05</td>\n",
       "      <td>3219</td>\n",
       "      <td>-68.341337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>2025-07-02</td>\n",
       "      <td>3977</td>\n",
       "      <td>689.658663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>2025-07-03</td>\n",
       "      <td>3835</td>\n",
       "      <td>547.658663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>2025-07-04</td>\n",
       "      <td>3861</td>\n",
       "      <td>573.658663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>2025-07-05</td>\n",
       "      <td>4145</td>\n",
       "      <td>919.417920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>2025-07-06</td>\n",
       "      <td>2807</td>\n",
       "      <td>-418.582080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>553 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tanggal  jumlah_penumpang_per_hari   residuals\n",
       "0   2024-01-01                       3166 -317.832049\n",
       "1   2024-01-02                       2901 -386.341337\n",
       "2   2024-01-03                       2795 -492.341337\n",
       "3   2024-01-04                       2784 -503.341337\n",
       "4   2024-01-05                       3219  -68.341337\n",
       "..         ...                        ...         ...\n",
       "548 2025-07-02                       3977  689.658663\n",
       "549 2025-07-03                       3835  547.658663\n",
       "550 2025-07-04                       3861  573.658663\n",
       "551 2025-07-05                       4145  919.417920\n",
       "552 2025-07-06                       2807 -418.582080\n",
       "\n",
       "[553 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals = model_ols.resid\n",
    "\n",
    "df_residuals = pd.DataFrame({\n",
    "    'residuals': residuals\n",
    "})\n",
    "\n",
    "df_results = df_train[['tanggal', 'jumlah_penumpang_per_hari']].reset_index(drop=True).copy()\n",
    "df_results['residuals'] = df_residuals['residuals'].values\n",
    "\n",
    "df_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a3d9579b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
    "plot_acf(residuals, ax=axes[0], lags=40)\n",
    "axes[0].set_title('ACF of Residuals')\n",
    "plot_pacf(residuals, ax=axes[1], lags=40, method='ywm')\n",
    "axes[1].set_title('PACF of Residuals')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07295dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seasonal strength (weekly): 0.405\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import STL\n",
    "import numpy as np\n",
    "\n",
    "# first 15 weeks\n",
    "seg = residuals.iloc[:7*15].astype(float).copy()\n",
    "\n",
    "# STL on residuals (weekly period)\n",
    "stl = STL(seg, period=7, robust=True).fit()\n",
    "fig = stl.plot()\n",
    "fig.suptitle(\"STL Decomposition — Residuals (first 15 weeks, period=7)\", y=1.02)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Optional: quantify seasonal strength (0..1; higher = stronger)\n",
    "seasonal_strength = 1 - (np.var(stl.resid) / np.var(stl.resid + stl.seasonal))\n",
    "print(f\"Seasonal strength (weekly): {seasonal_strength:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e44ba9c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take first 15 weeks (7*15 = 105 obs)\n",
    "seg = residuals.iloc[:7*15].copy()\n",
    "\n",
    "# Positions 0..N-1 and week boundaries (exclude 0; include 7,14,...)\n",
    "xpos = np.arange(len(seg))\n",
    "week_bounds = np.arange(7, len(seg), 7)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 3.5))\n",
    "ax.plot(xpos, seg.values, lw=1.5)\n",
    "ax.set_title(\"Residuals — First 15 Weeks\")\n",
    "ax.set_ylabel(\"Residual\")\n",
    "ax.set_xlabel(\"Day (first 105 obs)\")\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# Vertical lines at each multiple of 7 (week boundary)\n",
    "for xb in week_bounds:\n",
    "    ax.axvline(x=xb, linestyle=\"--\", alpha=0.4)\n",
    "\n",
    "# Optional: label week numbers at boundaries\n",
    "for i, xb in enumerate(week_bounds, start=1):\n",
    "    ax.text(xb + 0.2, ax.get_ylim()[1]*0.95, f\"W{i+0}\", fontsize=8, rotation=90, alpha=0.6)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5e00506e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7      -10.509288\n",
       "8      156.000000\n",
       "9      198.000000\n",
       "10     168.000000\n",
       "11      23.000000\n",
       "          ...    \n",
       "548    354.000000\n",
       "549    318.000000\n",
       "550    902.314196\n",
       "551    449.823484\n",
       "552   -528.176516\n",
       "Length: 546, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals_seasonal_diff = residuals.diff(7).dropna()\n",
    "residuals_seasonal_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "50f6c8c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
    "plot_acf(residuals_seasonal_diff, ax=axes[0], lags=40)\n",
    "axes[0].set_title('ACF of Residuals_residuals_seasonal_diff')\n",
    "plot_pacf(residuals_seasonal_diff, ax=axes[1], lags=40, method='ywm')\n",
    "axes[1].set_title('PACF of Residuals')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc5590f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -6.65723818973066\n",
      "p-value: 4.948788800306484e-09\n",
      "Critical Value (1%): -3.4427251295084678\n",
      "Critical Value (5%): -2.8669984098683736\n",
      "Critical Value (10%): -2.5696771375119254\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "adf_result = adfuller(residuals_seasonal_diff)\n",
    "print('ADF Statistic:', adf_result[0])\n",
    "print('p-value:', adf_result[1])\n",
    "for key, value in adf_result[4].items():\n",
    "    print(f'Critical Value ({key}): {value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f9aca655",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "λ̂ (MLE): 1.2266608436929325\n",
      "95% CI for λ: [1.0636, 1.4037]\n",
      "Does CI contain 1?  False\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import boxcox, boxcox_llf, boxcox_normmax, chi2\n",
    "\n",
    "# 1) Clean and ensure positivity\n",
    "x = residuals_seasonal_diff.dropna().astype(float).values\n",
    "x = x[np.isfinite(x)]\n",
    "if (x <= 0).any():\n",
    "    # shift so min becomes 1 (strictly positive)\n",
    "    x_pos = x + (1 - x.min())\n",
    "else:\n",
    "    x_pos = x\n",
    "\n",
    "# 2) Estimate lambda on the same positive data\n",
    "lam_hat = boxcox_normmax(x_pos, method='mle')  # or use boxcox(...)[1]\n",
    "_ = boxcox(x_pos, lam_hat)  # just to verify it works; not used further\n",
    "\n",
    "# 3) Profile log-likelihood and CI\n",
    "def llf(lmbda):\n",
    "    return boxcox_llf(lmbda, x_pos)\n",
    "\n",
    "llf_max = llf(lam_hat)\n",
    "crit = 0.5 * chi2.ppf(0.95, df=1)  # = 0.5 * 3.841...\n",
    "\n",
    "# adaptive grid around lam_hat\n",
    "width = 2.0\n",
    "for _ in range(6):  # expand up to ±64 if needed\n",
    "    grid = np.linspace(lam_hat - width, lam_hat + width, 2000)\n",
    "    vals = np.array([llf(l) for l in grid])\n",
    "    mask = vals >= (llf_max - crit)\n",
    "    if mask.any():\n",
    "        ci_lams = grid[mask]\n",
    "        ci_low, ci_high = ci_lams[0], ci_lams[-1]\n",
    "        break\n",
    "    width *= 2\n",
    "else:\n",
    "    raise RuntimeError(\"Failed to find CI: widen search or check data quality.\")\n",
    "\n",
    "print(f\"λ̂ (MLE): {lam_hat}\")\n",
    "print(f\"95% CI for λ: [{ci_low:.4f}, {ci_high:.4f}]\")\n",
    "print(\"Does CI contain 1? \", (ci_low <= 1.0 <= ci_high))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "41bd4863",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             model     order seasonal_order  folds    AIC_mean  AIC_median  MAPE_mean_%  MAPE_median_%  LB_pass_rate\n",
      "(1,0,1)x(0,1,1)[7] (1, 0, 1)   (0, 1, 1, 7)     67 5613.632232 5617.223845    11.262248       8.858762      0.955224\n",
      "(2,0,0)x(0,1,1)[7] (2, 0, 0)   (0, 1, 1, 7)     67 5642.352520 5638.694546    11.438061       8.625232      0.104478\n",
      "(0,0,1)x(0,1,1)[7] (0, 0, 1)   (0, 1, 1, 7)     67 5645.107148 5657.026285    11.268855       9.636759      0.000000\n",
      "(1,0,0)x(0,1,1)[7] (1, 0, 0)   (0, 1, 1, 7)     67 5650.519083 5657.777714    11.212604       8.729855      0.000000\n",
      "(1,0,0)x(1,1,1)[7] (1, 0, 0)   (1, 1, 1, 7)     67 5650.661135 5653.040448    11.341411       8.743947      0.000000\n",
      "(1,0,0)x(1,1,0)[7] (1, 0, 0)   (1, 1, 0, 7)     67 5666.412635 5671.229912    11.950916      10.203170      0.000000\n"
     ]
    }
   ],
   "source": [
    "import numpy as np, pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "from scipy.stats import boxcox\n",
    "from scipy.special import inv_boxcox  # for inverse Box–Cox\n",
    "\n",
    "# -----------------------------\n",
    "# 0) Prepare y and X (with Box–Cox)\n",
    "# -----------------------------\n",
    "S = 7\n",
    "target_col = \"jumlah_penumpang_per_hari\"\n",
    "date_col   = \"tanggal\"\n",
    "\n",
    "df_ = df_train.copy()\n",
    "df_[date_col] = pd.to_datetime(df_[date_col])\n",
    "df_ = df_.sort_values(date_col).set_index(date_col).asfreq(\"D\")\n",
    "\n",
    "# original y (for scoring)\n",
    "y_full = df_[target_col].astype(float).interpolate(limit_direction=\"both\")\n",
    "\n",
    "# ensure positivity for Box–Cox\n",
    "if (y_full <= 0).any():\n",
    "    bc_shift = 1.0 - y_full.min()\n",
    "else:\n",
    "    bc_shift = 0.0\n",
    "\n",
    "y_pos = y_full + bc_shift\n",
    "\n",
    "# lam_hat is assumed to be defined from your earlier Box–Cox code\n",
    "# lam_hat = boxcox_normmax(...)\n",
    "\n",
    "y_bc_values = boxcox(y_pos.values, lmbda=lam_hat)\n",
    "y_bc_full = pd.Series(y_bc_values, index=y_full.index, name=f\"{target_col}_bc\")\n",
    "\n",
    "# Exogenous\n",
    "X_full = df_.drop(columns=[target_col])  # keep everything else\n",
    "if date_col in X_full.columns: \n",
    "    X_full = X_full.drop(columns=[date_col])\n",
    "\n",
    "X_full = sm.add_constant(X_full, has_constant=\"add\")\n",
    "\n",
    "# (optionally) predefined split; here you use all for CV\n",
    "y_train      = y_full.copy()     # original scale\n",
    "y_bc_train   = y_bc_full.copy()  # Box–Cox scale\n",
    "X_train      = X_full.copy()\n",
    "\n",
    "# -----------------------------\n",
    "# 1) Candidate model list\n",
    "# -----------------------------\n",
    "CANDIDATES = [\n",
    "    {\"name\": \"(1,0,0)x(0,1,1)[7]\", \"order\": (1,0,0), \"seasonal_order\": (0,1,1,S)},\n",
    "    {\"name\": \"(0,0,1)x(0,1,1)[7]\", \"order\": (0,0,1), \"seasonal_order\": (0,1,1,S)},\n",
    "    {\"name\": \"(1,0,1)x(0,1,1)[7]\", \"order\": (1,0,1), \"seasonal_order\": (0,1,1,S)},\n",
    "    {\"name\": \"(2,0,0)x(0,1,1)[7]\", \"order\": (2,0,0), \"seasonal_order\": (0,1,1,S)},\n",
    "    {\"name\": \"(1,0,0)x(1,1,0)[7]\", \"order\": (1,0,0), \"seasonal_order\": (1,1,0,S)},\n",
    "    {\"name\": \"(1,0,0)x(1,1,1)[7]\", \"order\": (1,0,0), \"seasonal_order\": (1,1,1,S)},\n",
    "]\n",
    "\n",
    "# -----------------------------\n",
    "# 2) Helpers\n",
    "# -----------------------------\n",
    "def fit_sarimax(y_bc, X, order, seasonal_order):\n",
    "    \"\"\"Fit SARIMAX on Box–Cox transformed y.\"\"\"\n",
    "    mod = SARIMAX(\n",
    "        y_bc, exog=X, order=order, seasonal_order=seasonal_order,\n",
    "        trend=\"n\",  # intercept already in X\n",
    "        enforce_stationarity=False, enforce_invertibility=False\n",
    "    )\n",
    "    res = mod.fit(disp=False)\n",
    "    return res\n",
    "\n",
    "def mape(y_true, y_pred):\n",
    "    y_true = np.asarray(y_true, float)\n",
    "    y_pred = np.asarray(y_pred, float)\n",
    "    mask = (y_true != 0) & np.isfinite(y_true) & np.isfinite(y_pred)\n",
    "    if not np.any(mask): \n",
    "        return np.nan\n",
    "    return float(np.mean(np.abs((y_true[mask]-y_pred[mask]) / y_true[mask])) * 100.0)\n",
    "\n",
    "def lb_pass(resid, lags=14, alpha=0.05):\n",
    "    resid = pd.Series(resid).dropna()\n",
    "    if len(resid) < lags + 5:\n",
    "        return np.nan  # not enough data\n",
    "    p = acorr_ljungbox(resid, lags=[lags], return_df=True)[\"lb_pvalue\"].iloc[0]\n",
    "    return bool(p > alpha)\n",
    "\n",
    "# -----------------------------\n",
    "# 3) Expanding-window CV on Box–Cox scale\n",
    "#     - Fit on y_bc\n",
    "#     - Forecast on Box–Cox scale\n",
    "#     - Inverse Box–Cox + unshift for MAPE in original scale\n",
    "# -----------------------------\n",
    "def expanding_cv_scores(\n",
    "    y_orig, y_bc, X, candidates,\n",
    "    horizon=7, step=7,\n",
    "    min_train=max(12*S, 84),\n",
    "    lam=lam_hat, shift=bc_shift\n",
    "):\n",
    "    # Align\n",
    "    y_orig = y_orig.asfreq(\"D\").interpolate(limit_direction=\"both\")\n",
    "    y_bc   = y_bc.reindex(y_orig.index)\n",
    "    X      = X.reindex(y_orig.index).fillna(0)\n",
    "\n",
    "    results = []\n",
    "    n = len(y_bc)\n",
    "\n",
    "    for cand in candidates:\n",
    "        aics, mapes, lb_flags = [], [], []\n",
    "\n",
    "        for end in range(min_train, n - horizon + 1, step):\n",
    "            # TRAIN: Box–Cox scale\n",
    "            y_tr_bc = y_bc.iloc[:end]\n",
    "            # TEST: original scale for scoring\n",
    "            y_te_orig = y_orig.iloc[end:end + horizon]\n",
    "\n",
    "            X_tr = X.iloc[:end]\n",
    "            X_te = X.iloc[end:end + horizon]\n",
    "\n",
    "            try:\n",
    "                res = fit_sarimax(y_tr_bc, X_tr, cand[\"order\"], cand[\"seasonal_order\"])\n",
    "            except Exception as e:\n",
    "                aics.append(np.inf)\n",
    "                mapes.append(np.nan)\n",
    "                lb_flags.append(False)\n",
    "                continue\n",
    "\n",
    "            # AIC on TRAIN fit\n",
    "            aics.append(float(res.aic))\n",
    "\n",
    "            # Forecast on Box–Cox scale\n",
    "            fc_bc = res.get_forecast(steps=len(y_te_orig), exog=X_te).predicted_mean\n",
    "\n",
    "            # Inverse Box–Cox -> back to \"positive\" scale\n",
    "            fc_pos = inv_boxcox(fc_bc.values, lam)\n",
    "            # Undo shift to get original scale\n",
    "            fc_orig = fc_pos - shift\n",
    "\n",
    "            # MAPE in original scale\n",
    "            mapes.append(mape(y_te_orig.values, fc_orig))\n",
    "\n",
    "            # Ljung–Box on transformed residuals\n",
    "            lb_flags.append(lb_pass(res.resid, lags=14, alpha=0.05))\n",
    "\n",
    "        results.append({\n",
    "            \"model\": cand[\"name\"],\n",
    "            \"order\": cand[\"order\"],\n",
    "            \"seasonal_order\": cand[\"seasonal_order\"],\n",
    "            \"folds\": len(aics),\n",
    "            \"AIC_mean\": np.nanmean(aics),\n",
    "            \"AIC_median\": np.nanmedian(aics),\n",
    "            \"MAPE_mean_%\": np.nanmean(mapes),\n",
    "            \"MAPE_median_%\": np.nanmedian(mapes),\n",
    "            \"LB_pass_rate\": np.mean([x is True for x in lb_flags]) if len(lb_flags) > 0 else np.nan\n",
    "        })\n",
    "\n",
    "    return pd.DataFrame(results).sort_values([\"AIC_mean\", \"MAPE_mean_%\"]).reset_index(drop=True)\n",
    "\n",
    "# -----------------------------\n",
    "# 4) Run CV and print table\n",
    "# -----------------------------\n",
    "scores = expanding_cv_scores(\n",
    "    y_train,      # original y\n",
    "    y_bc_train,   # Box–Cox transformed y\n",
    "    X_train,\n",
    "    CANDIDATES,\n",
    "    horizon=7,\n",
    "    step=7,\n",
    "    min_train=max(12*S, 84),\n",
    "    lam=lam_hat,\n",
    "    shift=bc_shift\n",
    ")\n",
    "print(scores.to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9b441ea5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                     SARIMAX Results                                     \n",
      "=========================================================================================\n",
      "Dep. Variable:      jumlah_penumpang_per_hari_bc   No. Observations:                  553\n",
      "Model:             SARIMAX(1, 0, 1)x(0, 1, 1, 7)   Log Likelihood               -5053.355\n",
      "Date:                           Tue, 02 Dec 2025   AIC                          10126.710\n",
      "Time:                                   16:35:30   BIC                          10169.570\n",
      "Sample:                               01-01-2024   HQIC                         10143.477\n",
      "                                    - 07-06-2025                                         \n",
      "Covariance Type:                          robust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "const                          4.7177   8442.379      0.001      1.000   -1.65e+04    1.66e+04\n",
      "is_weekend                     2.4750   4631.231      0.001      1.000   -9074.570    9079.520\n",
      "is_holiday_nat               542.3279    698.282      0.777      0.437    -826.279    1910.935\n",
      "flag_contiguous_off        -2180.2337    726.106     -3.003      0.003   -3603.375    -757.092\n",
      "flag_almost_contiguous_off  -525.3621    751.919     -0.699      0.485   -1999.096     948.372\n",
      "events                      3.026e+04   2123.491     14.248      0.000    2.61e+04    3.44e+04\n",
      "ar.L1                          0.8187      0.044     18.781      0.000       0.733       0.904\n",
      "ma.L1                         -0.4620      0.099     -4.676      0.000      -0.656      -0.268\n",
      "ma.S.L7                       -0.7464      0.043    -17.365      0.000      -0.831      -0.662\n",
      "sigma2                      8.653e+06      4.291   2.02e+06      0.000    8.65e+06    8.65e+06\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   1.64   Jarque-Bera (JB):              1413.10\n",
      "Prob(Q):                              0.20   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               1.50   Skew:                             1.18\n",
      "Prob(H) (two-sided):                  0.01   Kurtosis:                        10.59\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Quasi-maximum likelihood covariance matrix used for robustness to some misspecifications; calculated using the observed information matrix (complex-step) described in Harvey (1989).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 1.47e+22. Standard errors may be unstable.\n",
      "\n",
      "Test MAPE (original scale, %): 8.641\n"
     ]
    }
   ],
   "source": [
    "import numpy as np, pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "from scipy.stats import boxcox\n",
    "from scipy.special import inv_boxcox  # inverse Box–Cox\n",
    "\n",
    "# -----------------------------\n",
    "# 0) Prepare y and X for TRAIN\n",
    "# -----------------------------\n",
    "S = 7\n",
    "target_col = \"jumlah_penumpang_per_hari\"\n",
    "date_col   = \"tanggal\"\n",
    "\n",
    "# TRAIN\n",
    "df_tr = df_train.copy()\n",
    "df_tr[date_col] = pd.to_datetime(df_tr[date_col])\n",
    "df_tr = df_tr.sort_values(date_col).set_index(date_col).asfreq(\"D\")\n",
    "\n",
    "y_train_orig = df_tr[target_col].astype(float).interpolate(limit_direction=\"both\")\n",
    "X_train = df_tr.drop(columns=[target_col])\n",
    "\n",
    "if date_col in X_train.columns:\n",
    "    X_train = X_train.drop(columns=[date_col])\n",
    "\n",
    "X_train = sm.add_constant(X_train, has_constant=\"add\")\n",
    "\n",
    "# -----------------------------\n",
    "# Box–Cox transform y_train using lam_hat\n",
    "# (assumes lam_hat already defined from your Box–Cox code)\n",
    "# -----------------------------\n",
    "if (y_train_orig <= 0).any():\n",
    "    bc_shift = 1.0 - y_train_orig.min()\n",
    "else:\n",
    "    bc_shift = 0.0\n",
    "\n",
    "y_train_pos = y_train_orig + bc_shift\n",
    "y_train_bc_vals = boxcox(y_train_pos.values, lmbda=lam_hat)\n",
    "y_train_bc = pd.Series(y_train_bc_vals, index=y_train_orig.index, name=f\"{target_col}_bc\")\n",
    "\n",
    "# -----------------------------\n",
    "# 1) Prepare TEST set\n",
    "# -----------------------------\n",
    "df_te = df_test.copy()\n",
    "df_te[date_col] = pd.to_datetime(df_te[date_col])\n",
    "df_te = df_te.sort_values(date_col).set_index(date_col).asfreq(\"D\")\n",
    "\n",
    "y_test_orig = df_te[target_col].astype(float).interpolate(limit_direction=\"both\")\n",
    "X_test = df_te.drop(columns=[target_col])\n",
    "\n",
    "if date_col in X_test.columns:\n",
    "    X_test = X_test.drop(columns=[date_col])\n",
    "\n",
    "X_test = sm.add_constant(X_test, has_constant=\"add\")\n",
    "\n",
    "# ensure X_test has same columns/order as X_train\n",
    "X_test = X_test.reindex(columns=X_train.columns, fill_value=0)\n",
    "\n",
    "# -----------------------------\n",
    "# 2) Fit the best SARIMAX model on Box–Cox y\n",
    "# -----------------------------\n",
    "best_order = (1, 0, 1)\n",
    "best_seasonal_order = (0, 1, 1, S)\n",
    "\n",
    "model_best = SARIMAX(\n",
    "    y_train_bc,\n",
    "    exog=X_train,\n",
    "    order=best_order,\n",
    "    seasonal_order=best_seasonal_order,\n",
    "    trend=\"n\",\n",
    "    enforce_stationarity=False,\n",
    "    enforce_invertibility=False\n",
    ")\n",
    "\n",
    "results_best = model_best.fit(disp=False, cov_type='robust')\n",
    "print(results_best.summary())\n",
    "\n",
    "# -----------------------------\n",
    "# 3) Forecast on TEST, inverse-transform, compute MAPE\n",
    "# -----------------------------\n",
    "def mape(y_true, y_pred):\n",
    "    y_true = np.asarray(y_true, float)\n",
    "    y_pred = np.asarray(y_pred, float)\n",
    "    mask = (y_true != 0) & np.isfinite(y_true) & np.isfinite(y_pred)\n",
    "    if not np.any(mask):\n",
    "        return np.nan\n",
    "    return float(np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100.0)\n",
    "\n",
    "# forecast on Box–Cox scale\n",
    "n_test = len(y_test_orig)\n",
    "fc_bc = results_best.get_forecast(steps=n_test, exog=X_test).predicted_mean\n",
    "\n",
    "# inverse Box–Cox back to positive scale\n",
    "fc_pos = inv_boxcox(fc_bc.values, lam_hat)\n",
    "\n",
    "# undo shift to get back to original scale\n",
    "fc_orig = fc_pos - bc_shift\n",
    "\n",
    "# compute MAPE vs original test y\n",
    "test_mape = mape(y_test_orig.values, fc_orig)\n",
    "print(f\"\\nTest MAPE (original scale, %): {test_mape:.3f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ccfa0d52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Ljung-Box (test of no autocorrelation) ===\n",
      "      lb_stat  lb_pvalue\n",
      "7    3.057388   0.879643\n",
      "14   5.191549   0.982964\n",
      "28  14.572920   0.982599\n",
      "\n",
      "=== ARCH test (heteroscedasticity) ===\n",
      "LM stat=45.4452, LM p-value=0.0000, F p-value=0.0000\n",
      "\n",
      "=== Normality tests ===\n",
      "Jarque–Bera: stat=1480.4251, p-value=0.0000, skew=1.1935, kurtosis=10.6519\n",
      "Shapiro-Wilk: stat=0.8960, p-value=0.0000\n",
      "\n",
      "Skew / Kurtosis (excess): 1.193531563073356 7.651917721074668\n",
      "\n",
      "=== Stationarity (ADF test) ===\n",
      "ADF stat=-23.3397, p-value=0.0000\n",
      "  Critical value (1%): -3.4423\n",
      "  Critical value (5%): -2.8668\n",
      "  Critical value (10%): -2.5696\n",
      "\n",
      "--- Quick interpretation hints ---\n",
      "- Ljung-Box p-values > 0.05 at tested lags -> no evidence of autocorrelation (white noise)\n",
      "- ARCH p-value < 0.05 -> evidence of heteroscedasticity\n",
      "- JB / Shapiro p-value < 0.05 -> residuals deviate from normality\n",
      "- ADF p-value < 0.05 -> residuals appear stationary\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "from statsmodels.stats.diagnostic import het_arch\n",
    "from statsmodels.stats.stattools import jarque_bera\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "# Residual diagnostics: white noise (Ljung-Box), homoscedasticity (ARCH), normality (JB/Shapiro), stationarity (ADF)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# pick residuals: prefer fitted SARIMAX residuals if available\n",
    "res = results_best.resid\n",
    "\n",
    "res = pd.Series(res).dropna().astype(float)\n",
    "std_res = (res - res.mean()) / res.std()\n",
    "\n",
    "# Plots\n",
    "fig, axs = plt.subplots(2, 3, figsize=(15, 8))\n",
    "ax = axs.ravel()\n",
    "\n",
    "# Time series\n",
    "res.plot(ax=ax[0], title=\"Residuals (time series)\")\n",
    "ax[0].axhline(0, color=\"k\", lw=0.8)\n",
    "\n",
    "# Histogram + KDE\n",
    "ax[1].hist(res, bins=40, density=True, alpha=0.6)\n",
    "kde = stats.gaussian_kde(res)\n",
    "xs = np.linspace(res.min(), res.max(), 200)\n",
    "ax[1].plot(xs, kde(xs), color=\"C1\")\n",
    "ax[1].set_title(\"Histogram + KDE\")\n",
    "\n",
    "# QQ-plot\n",
    "stats.probplot(std_res, dist=\"norm\", plot=ax[2])\n",
    "ax[2].set_title(\"QQ-plot (standardized residuals)\")\n",
    "\n",
    "# ACF / PACF\n",
    "plot_acf(std_res, ax=ax[3], lags=40, title=\"ACF (standardized residuals)\")\n",
    "plot_pacf(std_res, ax=ax[4], lags=40, method=\"ywm\", title=\"PACF (standardized residuals)\")\n",
    "\n",
    "# Rolling volatility to inspect heteroscedasticity\n",
    "rolling_std = res.rolling(window=30, min_periods=5).std()\n",
    "rolling_std.plot(ax=ax[5], title=\"Rolling 30-day std (volatility)\")\n",
    "ax[5].set_ylabel(\"Std\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Statistical tests\n",
    "print(\"=== Ljung-Box (test of no autocorrelation) ===\")\n",
    "lb = acorr_ljungbox(res, lags=[7,14,28], return_df=True)\n",
    "print(lb)\n",
    "\n",
    "print(\"\\n=== ARCH test (heteroscedasticity) ===\")\n",
    "arch_lm, arch_p, arch_f, arch_fp = het_arch(res, nlags=12)\n",
    "print(f\"LM stat={arch_lm:.4f}, LM p-value={arch_p:.4f}, F p-value={arch_fp:.4f}\")\n",
    "\n",
    "print(\"\\n=== Normality tests ===\")\n",
    "jb_stat, jb_p, jb_skew, jb_kurt = jarque_bera(res)\n",
    "print(f\"Jarque–Bera: stat={jb_stat:.4f}, p-value={jb_p:.4f}, skew={jb_skew:.4f}, kurtosis={jb_kurt:.4f}\")\n",
    "try:\n",
    "    sh_stat, sh_p = stats.shapiro(res)\n",
    "    print(f\"Shapiro-Wilk: stat={sh_stat:.4f}, p-value={sh_p:.4f}\")\n",
    "except Exception as e:\n",
    "    print(\"Shapiro-Wilk not available:\", e)\n",
    "\n",
    "print(\"\\nSkew / Kurtosis (excess):\", stats.skew(res), stats.kurtosis(res, fisher=True))\n",
    "\n",
    "print(\"\\n=== Stationarity (ADF test) ===\")\n",
    "adf_res = adfuller(res)\n",
    "print(f\"ADF stat={adf_res[0]:.4f}, p-value={adf_res[1]:.4f}\")\n",
    "for k, v in adf_res[4].items():\n",
    "    print(f\"  Critical value ({k}): {v:.4f}\")\n",
    "\n",
    "# Short interpretation hints\n",
    "print(\"\\n--- Quick interpretation hints ---\")\n",
    "print(\"- Ljung-Box p-values > 0.05 at tested lags -> no evidence of autocorrelation (white noise)\")\n",
    "print(\"- ARCH p-value < 0.05 -> evidence of heteroscedasticity\")\n",
    "print(\"- JB / Shapiro p-value < 0.05 -> residuals deviate from normality\")\n",
    "print(\"- ADF p-value < 0.05 -> residuals appear stationary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6829104e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                      SARIMAX Results                                      \n",
      "===========================================================================================\n",
      "Dep. Variable:        jumlah_penumpang_per_hari_bc   No. Observations:                  553\n",
      "Model:             SARIMAX(1, 0, 2)x(0, 1, [1], 7)   Log Likelihood               -5057.492\n",
      "Date:                             Tue, 02 Dec 2025   AIC                          10136.983\n",
      "Time:                                     16:37:08   BIC                          10184.109\n",
      "Sample:                                 01-01-2024   HQIC                         10155.420\n",
      "                                      - 07-06-2025                                         \n",
      "Covariance Type:                            robust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "const                       1.268e-05   9901.499   1.28e-09      1.000   -1.94e+04    1.94e+04\n",
      "is_weekend                   7.96e-06   3654.591   2.18e-09      1.000   -7162.867    7162.867\n",
      "is_holiday_nat               530.8654    753.133      0.705      0.481    -945.248    2006.978\n",
      "flag_contiguous_off        -2168.5476    772.738     -2.806      0.005   -3683.086    -654.009\n",
      "flag_almost_contiguous_off  -536.7176    770.260     -0.697      0.486   -2046.400     972.964\n",
      "events                      3.025e+04   2247.275     13.462      0.000    2.58e+04    3.47e+04\n",
      "ar.L1                          0.9029      0.030     30.433      0.000       0.845       0.961\n",
      "ma.L1                         -0.4963      0.086     -5.779      0.000      -0.665      -0.328\n",
      "ma.L2                         -0.1668      0.058     -2.895      0.004      -0.280      -0.054\n",
      "ma.S.L7                       -0.7730      0.043    -18.013      0.000      -0.857      -0.689\n",
      "sigma2                      1.273e+07      1.760   7.23e+06      0.000    1.27e+07    1.27e+07\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.02   Jarque-Bera (JB):              1453.53\n",
      "Prob(Q):                              0.89   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               1.46   Skew:                             1.28\n",
      "Prob(H) (two-sided):                  0.01   Kurtosis:                        10.65\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Quasi-maximum likelihood covariance matrix used for robustness to some misspecifications; calculated using the observed information matrix (complex-step) described in Harvey (1989).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 1.44e+23. Standard errors may be unstable.\n",
      "\n",
      "Test MAPE (original scale, %): 8.405\n"
     ]
    }
   ],
   "source": [
    "import numpy as np, pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "from scipy.stats import boxcox\n",
    "from scipy.special import inv_boxcox  # inverse Box–Cox\n",
    "\n",
    "# -----------------------------\n",
    "# 0) Prepare y and X for TRAIN\n",
    "# -----------------------------\n",
    "S = 7\n",
    "target_col = \"jumlah_penumpang_per_hari\"\n",
    "date_col   = \"tanggal\"\n",
    "\n",
    "# TRAIN\n",
    "df_tr = df_train.copy()\n",
    "df_tr[date_col] = pd.to_datetime(df_tr[date_col])\n",
    "df_tr = df_tr.sort_values(date_col).set_index(date_col).asfreq(\"D\")\n",
    "\n",
    "y_train_orig = df_tr[target_col].astype(float).interpolate(limit_direction=\"both\")\n",
    "X_train = df_tr.drop(columns=[target_col])\n",
    "\n",
    "if date_col in X_train.columns:\n",
    "    X_train = X_train.drop(columns=[date_col])\n",
    "\n",
    "X_train = sm.add_constant(X_train, has_constant=\"add\")\n",
    "\n",
    "# -----------------------------\n",
    "# Box–Cox transform y_train using lam_hat\n",
    "# (assumes lam_hat already defined from your Box–Cox code)\n",
    "# -----------------------------\n",
    "if (y_train_orig <= 0).any():\n",
    "    bc_shift = 1.0 - y_train_orig.min()\n",
    "else:\n",
    "    bc_shift = 0.0\n",
    "\n",
    "y_train_pos = y_train_orig + bc_shift\n",
    "y_train_bc_vals = boxcox(y_train_pos.values, lmbda=lam_hat)\n",
    "y_train_bc = pd.Series(y_train_bc_vals, index=y_train_orig.index, name=f\"{target_col}_bc\")\n",
    "\n",
    "# -----------------------------\n",
    "# 1) Prepare TEST set\n",
    "# -----------------------------\n",
    "df_te = df_test.copy()\n",
    "df_te[date_col] = pd.to_datetime(df_te[date_col])\n",
    "df_te = df_te.sort_values(date_col).set_index(date_col).asfreq(\"D\")\n",
    "\n",
    "y_test_orig = df_te[target_col].astype(float).interpolate(limit_direction=\"both\")\n",
    "X_test = df_te.drop(columns=[target_col])\n",
    "\n",
    "if date_col in X_test.columns:\n",
    "    X_test = X_test.drop(columns=[date_col])\n",
    "\n",
    "X_test = sm.add_constant(X_test, has_constant=\"add\")\n",
    "\n",
    "# ensure X_test has same columns/order as X_train\n",
    "X_test = X_test.reindex(columns=X_train.columns, fill_value=0)\n",
    "\n",
    "# -----------------------------\n",
    "# 2) Fit the best SARIMAX model on Box–Cox y\n",
    "# -----------------------------\n",
    "best_order = (1, 0, 2)\n",
    "best_seasonal_order = (0, 1, 1, S)\n",
    "\n",
    "model_best = SARIMAX(\n",
    "    y_train_bc,\n",
    "    exog=X_train,\n",
    "    order=best_order,\n",
    "    seasonal_order=best_seasonal_order,\n",
    "    trend=\"n\",\n",
    "    enforce_stationarity=False,\n",
    "    enforce_invertibility=False\n",
    ")\n",
    "\n",
    "results_best = model_best.fit(disp=False, cov_type='robust')\n",
    "print(results_best.summary())\n",
    "\n",
    "# -----------------------------\n",
    "# 3) Forecast on TEST, inverse-transform, compute MAPE\n",
    "# -----------------------------\n",
    "def mape(y_true, y_pred):\n",
    "    y_true = np.asarray(y_true, float)\n",
    "    y_pred = np.asarray(y_pred, float)\n",
    "    mask = (y_true != 0) & np.isfinite(y_true) & np.isfinite(y_pred)\n",
    "    if not np.any(mask):\n",
    "        return np.nan\n",
    "    return float(np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100.0)\n",
    "\n",
    "# forecast on Box–Cox scale\n",
    "n_test = len(y_test_orig)\n",
    "fc_bc = results_best.get_forecast(steps=n_test, exog=X_test).predicted_mean\n",
    "\n",
    "# inverse Box–Cox back to positive scale\n",
    "fc_pos = inv_boxcox(fc_bc.values, lam_hat)\n",
    "\n",
    "# undo shift to get back to original scale\n",
    "fc_orig = fc_pos - bc_shift\n",
    "\n",
    "# compute MAPE vs original test y\n",
    "test_mape = mape(y_test_orig.values, fc_orig)\n",
    "print(f\"\\nTest MAPE (original scale, %): {test_mape:.3f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a2b560e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                     SARIMAX Results                                     \n",
      "=========================================================================================\n",
      "Dep. Variable:      jumlah_penumpang_per_hari_bc   No. Observations:                  553\n",
      "Model:             SARIMAX(2, 0, 1)x(0, 1, 1, 7)   Log Likelihood               -5065.659\n",
      "Date:                           Tue, 02 Dec 2025   AIC                          10153.319\n",
      "Time:                                   16:37:08   BIC                          10200.465\n",
      "Sample:                               01-01-2024   HQIC                         10171.762\n",
      "                                    - 07-06-2025                                         \n",
      "Covariance Type:                          robust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "const                       1.515e-05   1.04e+04   1.46e-09      1.000   -2.03e+04    2.03e+04\n",
      "is_weekend                  1.024e-05   3767.967   2.72e-09      1.000   -7385.080    7385.080\n",
      "is_holiday_nat               530.8654    747.198      0.710      0.477    -933.615    1995.346\n",
      "flag_contiguous_off        -2168.5476    781.018     -2.777      0.005   -3699.314    -637.781\n",
      "flag_almost_contiguous_off  -536.7176    769.476     -0.698      0.485   -2044.863     971.428\n",
      "events                      3.025e+04   2207.598     13.703      0.000    2.59e+04    3.46e+04\n",
      "ar.L1                          1.2102      0.103     11.746      0.000       1.008       1.412\n",
      "ar.L2                         -0.2640      0.088     -3.002      0.003      -0.436      -0.092\n",
      "ma.L1                         -0.7990      0.066    -12.128      0.000      -0.928      -0.670\n",
      "ma.S.L7                       -0.7805      0.041    -18.854      0.000      -0.862      -0.699\n",
      "sigma2                      1.264e+07      1.973    6.4e+06      0.000    1.26e+07    1.26e+07\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):              1521.65\n",
      "Prob(Q):                              0.97   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               1.45   Skew:                             1.28\n",
      "Prob(H) (two-sided):                  0.01   Kurtosis:                        10.84\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Quasi-maximum likelihood covariance matrix used for robustness to some misspecifications; calculated using the observed information matrix (complex-step) described in Harvey (1989).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 5.19e+23. Standard errors may be unstable.\n",
      "\n",
      "Test MAPE (original scale, %): 8.331\n"
     ]
    }
   ],
   "source": [
    "import numpy as np, pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "from scipy.stats import boxcox\n",
    "from scipy.special import inv_boxcox  # inverse Box–Cox\n",
    "\n",
    "# -----------------------------\n",
    "# 0) Prepare y and X for TRAIN\n",
    "# -----------------------------\n",
    "S = 7\n",
    "target_col = \"jumlah_penumpang_per_hari\"\n",
    "date_col   = \"tanggal\"\n",
    "\n",
    "# TRAIN\n",
    "df_tr = df_train.copy()\n",
    "df_tr[date_col] = pd.to_datetime(df_tr[date_col])\n",
    "df_tr = df_tr.sort_values(date_col).set_index(date_col).asfreq(\"D\")\n",
    "\n",
    "y_train_orig = df_tr[target_col].astype(float).interpolate(limit_direction=\"both\")\n",
    "X_train = df_tr.drop(columns=[target_col])\n",
    "\n",
    "if date_col in X_train.columns:\n",
    "    X_train = X_train.drop(columns=[date_col])\n",
    "\n",
    "X_train = sm.add_constant(X_train, has_constant=\"add\")\n",
    "\n",
    "# -----------------------------\n",
    "# Box–Cox transform y_train using lam_hat\n",
    "# (assumes lam_hat already defined from your Box–Cox code)\n",
    "# -----------------------------\n",
    "if (y_train_orig <= 0).any():\n",
    "    bc_shift = 1.0 - y_train_orig.min()\n",
    "else:\n",
    "    bc_shift = 0.0\n",
    "\n",
    "y_train_pos = y_train_orig + bc_shift\n",
    "y_train_bc_vals = boxcox(y_train_pos.values, lmbda=lam_hat)\n",
    "y_train_bc = pd.Series(y_train_bc_vals, index=y_train_orig.index, name=f\"{target_col}_bc\")\n",
    "\n",
    "# -----------------------------\n",
    "# 1) Prepare TEST set\n",
    "# -----------------------------\n",
    "df_te = df_test.copy()\n",
    "df_te[date_col] = pd.to_datetime(df_te[date_col])\n",
    "df_te = df_te.sort_values(date_col).set_index(date_col).asfreq(\"D\")\n",
    "\n",
    "y_test_orig = df_te[target_col].astype(float).interpolate(limit_direction=\"both\")\n",
    "X_test = df_te.drop(columns=[target_col])\n",
    "\n",
    "if date_col in X_test.columns:\n",
    "    X_test = X_test.drop(columns=[date_col])\n",
    "\n",
    "X_test = sm.add_constant(X_test, has_constant=\"add\")\n",
    "\n",
    "# ensure X_test has same columns/order as X_train\n",
    "X_test = X_test.reindex(columns=X_train.columns, fill_value=0)\n",
    "\n",
    "# -----------------------------\n",
    "# 2) Fit the best SARIMAX model on Box–Cox y\n",
    "# -----------------------------\n",
    "best_order = (2, 0, 1)\n",
    "best_seasonal_order = (0, 1, 1, S)\n",
    "\n",
    "model_best = SARIMAX(\n",
    "    y_train_bc,\n",
    "    exog=X_train,\n",
    "    order=best_order,\n",
    "    seasonal_order=best_seasonal_order,\n",
    "    trend=\"n\",\n",
    "    enforce_stationarity=False,\n",
    "    enforce_invertibility=False\n",
    ")\n",
    "\n",
    "results_best = model_best.fit(disp=False, cov_type='robust')\n",
    "print(results_best.summary())\n",
    "\n",
    "# -----------------------------\n",
    "# 3) Forecast on TEST, inverse-transform, compute MAPE\n",
    "# -----------------------------\n",
    "def mape(y_true, y_pred):\n",
    "    y_true = np.asarray(y_true, float)\n",
    "    y_pred = np.asarray(y_pred, float)\n",
    "    mask = (y_true != 0) & np.isfinite(y_true) & np.isfinite(y_pred)\n",
    "    if not np.any(mask):\n",
    "        return np.nan\n",
    "    return float(np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100.0)\n",
    "\n",
    "# forecast on Box–Cox scale\n",
    "n_test = len(y_test_orig)\n",
    "fc_bc = results_best.get_forecast(steps=n_test, exog=X_test).predicted_mean\n",
    "\n",
    "# inverse Box–Cox back to positive scale\n",
    "fc_pos = inv_boxcox(fc_bc.values, lam_hat)\n",
    "\n",
    "# undo shift to get back to original scale\n",
    "fc_orig = fc_pos - bc_shift\n",
    "\n",
    "# compute MAPE vs original test y\n",
    "test_mape = mape(y_test_orig.values, fc_orig)\n",
    "print(f\"\\nTest MAPE (original scale, %): {test_mape:.3f}\")\n"
   ]
  }
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