{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "edadd89f-0fc1-4e07-a249-2a0d4052258c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:             Trip count   No. Observations:                 7999\n",
      "Model:                 ARIMA(2, 1, 2)   Log Likelihood              -14325.727\n",
      "Date:                Thu, 26 Sep 2024   AIC                          28661.454\n",
      "Time:                        17:35:05   BIC                          28696.389\n",
      "Sample:                    04-01-2023   HQIC                         28673.412\n",
      "                         - 10-31-2023                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.4432      0.034     12.890      0.000       0.376       0.511\n",
      "ar.L2          0.2343      0.009     26.703      0.000       0.217       0.251\n",
      "ma.L1         -1.5914      0.035    -45.151      0.000      -1.660      -1.522\n",
      "ma.L2          0.5926      0.035     16.913      0.000       0.524       0.661\n",
      "sigma2         2.1040      0.015    136.783      0.000       2.074       2.134\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.14   Jarque-Bera (JB):             29710.16\n",
      "Prob(Q):                              0.71   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               0.77   Skew:                             2.39\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                        11.15\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "2024-02-28 07:00    0.438484\n",
      "Freq: h, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_csv('datasets/aggregated_2023_Designmuseo.csv')\n",
    "df['Departure'] = pd.to_datetime(df['Departure'], format='mixed')\n",
    "df['Trip count'] = pd.to_numeric(df['Trip count'])\n",
    "\n",
    "start_date = '2023-01-04 00:00'\n",
    "end_date = '2023-31-10 23:00'\n",
    "\n",
    "start = pd.to_datetime(start_date, format = '%Y-%d-%m %H:%M')\n",
    "end_date = pd.to_datetime(end_date, format = '%Y-%d-%m %H:%M')\n",
    "dates = pd.DataFrame({'Departure':pd.date_range(start, end_date, freq='h')})\n",
    "\n",
    "\n",
    "df3 = pd.concat([df, dates])\n",
    "values = {'Trip count' : 0, 'Departure station id' : 7.0, 'Departure station name' : 'Designmuseo'}\n",
    "df3.fillna(value=values, inplace = True)\n",
    "df3 = df3.sort_values(by='Departure')\n",
    "\n",
    "df3.set_index('Departure', inplace=True)\n",
    "df3.index = pd.DatetimeIndex(df3.index).to_period('h')\n",
    "\n",
    "model = ARIMA(df3['Trip count'], order=(2,1,2))\n",
    "model_fit = model.fit()\n",
    "# summary of fit model\n",
    "print(model_fit.summary())\n",
    "'''\n",
    "print(df3.sort_values(by='Departure'))\n",
    "plt.acorr(df3['Trip count'], maxlags = 1000)\n",
    "plt.grid(True)\n",
    "'''\n",
    "'''\n",
    "sm.graphics.tsa.plot_pacf(df3['Trip count'], lags =24, method='ywm')\n",
    "plt.show()\n",
    "'''\n",
    "print(model_fit.forecast(1))\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}