4a. Model Building - ARIMAX.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import seaborn as sns # data visualisation\n",
    "import matplotlib.pyplot as plt # data visualisation\n",
    "import datetime as dt # working with time data\n",
    "import plotly.graph_objs as go # plotly graphical object\n",
    "import statsmodels.api as sm #time-series analysis for python\n",
    "import warnings  \n",
    "import missingno as msno \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"../Data/gasoline_price_sentiment_pre-processed_7.xlsx\", \"Sheet1\")\n",
    "horizon = 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARIMAX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "buf = df.copy()#reset buf\n",
    "buf = buf.set_index('Date')\n",
    "buf.index = pd.DatetimeIndex(buf.index).to_period('D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Any Final Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "buf_before = buf.copy()\n",
    "msno.matrix(buf_before)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "def checkMissingVal(df):\n",
    "    i = 0\n",
    "    for val in buf.isna().sum():\n",
    "        if(val > 0):\n",
    "            print(f\"Missing value detected in column {buf.columns[i]}\")\n",
    "        i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing value detected in column price_lag_7\n",
      "Missing value detected in column price_rm\n"
     ]
    }
   ],
   "source": [
    "checkMissingVal(buf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'price', 'popularity', 'general_sentiment',\n",
       "       'positive_outlook', 'happiness', 'bittersweet', 'confidence', 'disgust',\n",
       "       'disagreement', 'anxiety', 'gloominess', 'distress', 'violent',\n",
       "       'unexpectedness', 'timeliness', 'confusion', 'feelingsVsTruth',\n",
       "       'buyVsSell', 'buyVsSellForecast', 'priceTrend', 'pricePrediction',\n",
       "       'volatile', 'consumptionCommodity', 'productionCommodity', 'regulatory',\n",
       "       'supplyDemandBalance', 'supplyDemandBalanceForecast', 'exploration',\n",
       "       'accidents', 'National_Holiday', 'Weekend', 'Holidays', 'happiness_Log',\n",
       "       'bittersweet_Log', 'disgust_Log', 'disagreement_Log', 'anxiety_Log',\n",
       "       'gloominess_Log', 'distress_Log', 'violent_Log', 'unexpectedness_Log',\n",
       "       'confusion_Log', 'buyVsSell_sqrt', 'pricePrediction_Log',\n",
       "       'volatile_Log', 'productionCommodity_Log', 'regulatory_Log',\n",
       "       'supplyDemandBalance_Log', 'exploration_Log', 'accidents_Log',\n",
       "       'month-1', 'month-2', 'month-3', 'month-4', 'month-5', 'month-6',\n",
       "       'month-7', 'month-8', 'month-9', 'month-10', 'month-11', 'month-12',\n",
       "       'dayofweek-0', 'dayofweek-1', 'dayofweek-2', 'dayofweek-3',\n",
       "       'dayofweek-4', 'dayofweek-5', 'dayofweek-6', 'weekend', 'sunday',\n",
       "       'price_lag_7', 'price_rm', 'price_ewm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_variable_columns = ['price', 'happiness_Log', 'bittersweet_Log',\n",
    "       'disgust_Log', 'disagreement_Log', 'anxiety_Log', 'gloominess_Log',\n",
    "       'distress_Log', 'violent_Log', 'unexpectedness_Log', 'confusion_Log',\n",
    "       'buyVsSell_sqrt', 'pricePrediction_Log', 'volatile_Log',\n",
    "       'productionCommodity_Log', 'regulatory_Log', 'supplyDemandBalance_Log',\n",
    "       'exploration_Log', 'accidents_Log', 'supplyDemandBalanceForecast', \n",
    "       'consumptionCommodity', 'priceTrend', 'buyVsSellForecast', 'feelingsVsTruth',\n",
    "       'timeliness', 'confidence', 'popularity', 'general_sentiment', 'positive_outlook',\n",
    "       'price_lag_7', 'month-1', 'month-2', \n",
    "       'month-3', 'month-4', 'month-5', 'month-6', 'month-7', 'month-8', \n",
    "       'month-9', 'month-10', 'month-11', 'month-12', 'dayofweek-0',\n",
    "       'dayofweek-1', 'dayofweek-2', 'dayofweek-3', 'dayofweek-4',\n",
    "       'dayofweek-5', 'dayofweek-6', 'weekend', 'sunday']\n",
    "\n",
    "#We are taking the transformed columns and the derived features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2629"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_size = int(len(buf) * 0.80)\n",
    "train_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "buf_test = buf.copy()\n",
    "buf_test.reset_index(inplace=True)\n",
    "tr_start = str(buf_test.iloc[0].Date.to_timestamp(freq='D'))[:10]\n",
    "tr_end = str(buf_test.iloc[train_size].Date.to_timestamp(freq='D'))[:10]\n",
    "te_start = str(buf_test.iloc[train_size+1].Date.to_timestamp(freq='D'))[:10]\n",
    "te_end = str(buf_test.iloc[-1].Date.to_timestamp(freq='D'))[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the end of train set date is almost close to end of March, we will just take until the end of that month (31st March) so that it is including the whole first quarter of the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_end = '2017-03-31'\n",
    "te_start = '2017-04-01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_start = '2010-01-08' #To remove the lagged rows with NA values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2010-01-08', '2017-03-31', '2017-04-01', '2018-12-31')"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr_start, tr_end, te_start, te_end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['happiness_Log', 'bittersweet_Log', 'disgust_Log', 'disagreement_Log',\n",
       "       'anxiety_Log', 'gloominess_Log', 'distress_Log', 'violent_Log',\n",
       "       'unexpectedness_Log', 'confusion_Log', 'buyVsSell_sqrt',\n",
       "       'pricePrediction_Log', 'volatile_Log', 'productionCommodity_Log',\n",
       "       'regulatory_Log', 'supplyDemandBalance_Log', 'exploration_Log',\n",
       "       'accidents_Log', 'supplyDemandBalanceForecast', 'consumptionCommodity',\n",
       "       'priceTrend', 'buyVsSellForecast', 'feelingsVsTruth', 'timeliness',\n",
       "       'confidence', 'popularity', 'general_sentiment', 'positive_outlook',\n",
       "       'price_lag_7', 'month-1', 'month-2', 'month-3', 'month-4', 'month-5',\n",
       "       'month-6', 'month-7', 'month-8', 'month-9', 'month-10', 'month-11',\n",
       "       'month-12', 'dayofweek-0', 'dayofweek-1', 'dayofweek-2', 'dayofweek-3',\n",
       "       'dayofweek-4', 'dayofweek-5', 'dayofweek-6', 'weekend', 'sunday'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buf = buf.loc[:, selected_variable_columns]\n",
    "exog_features = buf.drop(axis=1, columns='price').columns #to predict first week\n",
    "exog_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "tra = buf[tr_start:tr_end].loc[:, 'price']\n",
    "tes = buf[te_start:te_end].loc[:, 'price']\n",
    "exog_train = buf[tr_start:tr_end].loc[:, exog_features]\n",
    "exog_test = buf[te_start:te_end].loc[:, exog_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing value detected in column price_lag_7\n"
     ]
    }
   ],
   "source": [
    "checkMissingVal(tes) #Ensure all the values being passed to ARIMAX are not null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta, date\n",
    "horizon = 7\n",
    "\n",
    "test_horizon_slices = []\n",
    "i = 0\n",
    "cnt = 0\n",
    "for row in tes.rolling(window=7).mean().index:\n",
    "\n",
    "    if i % horizon > 0:\n",
    "        i+=1\n",
    "        continue\n",
    "    \n",
    "    cnt +=1\n",
    "    start = row\n",
    "    end = row + timedelta(horizon) - 1\n",
    "    startStr = str(start.to_timestamp(freq='D'))[:10]\n",
    "    endStr = str(end.to_timestamp(freq='D'))[:10]\n",
    "    test_horizon_slices.append((startStr, endStr))\n",
    "    i+=1\n",
    "\n",
    "# print(cnt)\n",
    "# test_horizon_slices[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train The Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>price</td>      <th>  No. Observations:  </th>   <td>2640</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 1)</td> <th>  Log Likelihood     </th> <td>-5409.140</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 10 Aug 2022</td> <th>  AIC                </th> <td>10924.280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:06:45</td>     <th>  BIC                </th> <td>11235.802</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-08-2010</td>    <th>  HQIC               </th> <td>11037.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 03-31-2017</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</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>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>happiness_Log</th>               <td>    6.8180</td> <td>   10.671</td> <td>    0.639</td> <td> 0.523</td> <td>  -14.097</td> <td>   27.733</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bittersweet_Log</th>             <td>    9.9593</td> <td>   10.027</td> <td>    0.993</td> <td> 0.321</td> <td>   -9.693</td> <td>   29.612</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>disgust_Log</th>                 <td>   11.2544</td> <td>    7.468</td> <td>    1.507</td> <td> 0.132</td> <td>   -3.382</td> <td>   25.891</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>disagreement_Log</th>            <td>   -5.6049</td> <td>    1.962</td> <td>   -2.857</td> <td> 0.004</td> <td>   -9.450</td> <td>   -1.760</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>anxiety_Log</th>                 <td>   16.0652</td> <td>    6.167</td> <td>    2.605</td> <td> 0.009</td> <td>    3.979</td> <td>   28.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gloominess_Log</th>              <td>   -5.4296</td> <td>    2.824</td> <td>   -1.923</td> <td> 0.054</td> <td>  -10.964</td> <td>    0.105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distress_Log</th>                <td>    7.0401</td> <td>    2.438</td> <td>    2.888</td> <td> 0.004</td> <td>    2.263</td> <td>   11.818</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>violent_Log</th>                 <td>    4.7138</td> <td>    1.672</td> <td>    2.819</td> <td> 0.005</td> <td>    1.436</td> <td>    7.991</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>unexpectedness_Log</th>          <td>   20.7752</td> <td>    6.172</td> <td>    3.366</td> <td> 0.001</td> <td>    8.679</td> <td>   32.871</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>confusion_Log</th>               <td>   13.1904</td> <td>    4.467</td> <td>    2.953</td> <td> 0.003</td> <td>    4.434</td> <td>   21.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>buyVsSell_sqrt</th>              <td>   28.6883</td> <td>    8.487</td> <td>    3.380</td> <td> 0.001</td> <td>   12.053</td> <td>   45.323</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pricePrediction_Log</th>         <td>   36.2900</td> <td>    5.705</td> <td>    6.361</td> <td> 0.000</td> <td>   25.107</td> <td>   47.472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>volatile_Log</th>                <td>    3.6858</td> <td>    2.646</td> <td>    1.393</td> <td> 0.164</td> <td>   -1.501</td> <td>    8.873</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>productionCommodity_Log</th>     <td>   -1.1640</td> <td>    4.238</td> <td>   -0.275</td> <td> 0.784</td> <td>   -9.471</td> <td>    7.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regulatory_Log</th>              <td>   57.5013</td> <td>   11.118</td> <td>    5.172</td> <td> 0.000</td> <td>   35.711</td> <td>   79.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>supplyDemandBalance_Log</th>     <td>   -5.5829</td> <td>    0.720</td> <td>   -7.758</td> <td> 0.000</td> <td>   -6.993</td> <td>   -4.173</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>exploration_Log</th>             <td>  -61.1171</td> <td>   21.112</td> <td>   -2.895</td> <td> 0.004</td> <td> -102.495</td> <td>  -19.739</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>accidents_Log</th>               <td>  -37.6504</td> <td>   16.131</td> <td>   -2.334</td> <td> 0.020</td> <td>  -69.266</td> <td>   -6.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>supplyDemandBalanceForecast</th> <td>   -8.5816</td> <td>    3.394</td> <td>   -2.529</td> <td> 0.011</td> <td>  -15.233</td> <td>   -1.930</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>consumptionCommodity</th>        <td>   -6.4691</td> <td>    7.063</td> <td>   -0.916</td> <td> 0.360</td> <td>  -20.312</td> <td>    7.374</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>priceTrend</th>                  <td>   15.8746</td> <td>    1.937</td> <td>    8.195</td> <td> 0.000</td> <td>   12.078</td> <td>   19.671</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>buyVsSellForecast</th>           <td>  -21.5008</td> <td>   12.464</td> <td>   -1.725</td> <td> 0.085</td> <td>  -45.931</td> <td>    2.929</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>feelingsVsTruth</th>             <td>   -0.1484</td> <td>    0.539</td> <td>   -0.275</td> <td> 0.783</td> <td>   -1.205</td> <td>    0.909</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>timeliness</th>                  <td>   -9.1035</td> <td>    2.099</td> <td>   -4.336</td> <td> 0.000</td> <td>  -13.218</td> <td>   -4.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>confidence</th>                  <td>  -10.5887</td> <td>    6.829</td> <td>   -1.550</td> <td> 0.121</td> <td>  -23.974</td> <td>    2.796</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popularity</th>                  <td>-1.733e-06</td> <td> 2.19e-05</td> <td>   -0.079</td> <td> 0.937</td> <td>-4.46e-05</td> <td> 4.11e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>general_sentiment</th>           <td>    3.7484</td> <td>    0.637</td> <td>    5.887</td> <td> 0.000</td> <td>    2.501</td> <td>    4.996</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>positive_outlook</th>            <td>   -4.7500</td> <td>    1.546</td> <td>   -3.073</td> <td> 0.002</td> <td>   -7.780</td> <td>   -1.720</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>price_lag_7</th>                 <td>    0.9818</td> <td>    0.008</td> <td>  116.649</td> <td> 0.000</td> <td>    0.965</td> <td>    0.998</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-1</th>                     <td>    4.2999</td> <td>    5.141</td> <td>    0.836</td> <td> 0.403</td> <td>   -5.776</td> <td>   14.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-2</th>                     <td>    5.5534</td> <td>    5.110</td> <td>    1.087</td> <td> 0.277</td> <td>   -4.461</td> <td>   15.568</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-3</th>                     <td>    3.9296</td> <td>    5.107</td> <td>    0.770</td> <td> 0.442</td> <td>   -6.079</td> <td>   13.938</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-4</th>                     <td>    4.8772</td> <td>    5.109</td> <td>    0.955</td> <td> 0.340</td> <td>   -5.135</td> <td>   14.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-5</th>                     <td>    3.3161</td> <td>    5.119</td> <td>    0.648</td> <td> 0.517</td> <td>   -6.717</td> <td>   13.349</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-6</th>                     <td>    3.8771</td> <td>    5.130</td> <td>    0.756</td> <td> 0.450</td> <td>   -6.178</td> <td>   13.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-7</th>                     <td>    4.5954</td> <td>    5.126</td> <td>    0.896</td> <td> 0.370</td> <td>   -5.452</td> <td>   14.642</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-8</th>                     <td>    4.5481</td> <td>    5.128</td> <td>    0.887</td> <td> 0.375</td> <td>   -5.503</td> <td>   14.599</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-9</th>                     <td>    4.3614</td> <td>    5.097</td> <td>    0.856</td> <td> 0.392</td> <td>   -5.629</td> <td>   14.352</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-10</th>                    <td>    4.0110</td> <td>    5.112</td> <td>    0.785</td> <td> 0.433</td> <td>   -6.007</td> <td>   14.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-11</th>                    <td>    4.0287</td> <td>    5.119</td> <td>    0.787</td> <td> 0.431</td> <td>   -6.004</td> <td>   14.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-12</th>                    <td>    4.7551</td> <td>    5.147</td> <td>    0.924</td> <td> 0.356</td> <td>   -5.333</td> <td>   14.843</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-0</th>                 <td>    9.1360</td> <td>   10.930</td> <td>    0.836</td> <td> 0.403</td> <td>  -12.287</td> <td>   30.559</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-1</th>                 <td>    9.2582</td> <td>   10.920</td> <td>    0.848</td> <td> 0.397</td> <td>  -12.144</td> <td>   30.660</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-2</th>                 <td>    9.6471</td> <td>   10.918</td> <td>    0.884</td> <td> 0.377</td> <td>  -11.751</td> <td>   31.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-3</th>                 <td>    9.4990</td> <td>   10.924</td> <td>    0.870</td> <td> 0.385</td> <td>  -11.912</td> <td>   30.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-4</th>                 <td>    9.2461</td> <td>   10.925</td> <td>    0.846</td> <td> 0.397</td> <td>  -12.167</td> <td>   30.660</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-5</th>                 <td>    3.6111</td> <td>    4.367</td> <td>    0.827</td> <td> 0.408</td> <td>   -4.947</td> <td>   12.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-6</th>                 <td>    1.7554</td> <td>    2.188</td> <td>    0.802</td> <td> 0.422</td> <td>   -2.533</td> <td>    6.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weekend</th>                     <td>    5.3666</td> <td>    6.554</td> <td>    0.819</td> <td> 0.413</td> <td>   -7.478</td> <td>   18.211</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sunday</th>                      <td>    1.7554</td> <td>    2.188</td> <td>    0.802</td> <td> 0.422</td> <td>   -2.533</td> <td>    6.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>                       <td>    0.8379</td> <td>    0.012</td> <td>   67.732</td> <td> 0.000</td> <td>    0.814</td> <td>    0.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>                       <td>   -0.0285</td> <td>    0.022</td> <td>   -1.281</td> <td> 0.200</td> <td>   -0.072</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>                      <td>    3.5474</td> <td>    0.089</td> <td>   40.038</td> <td> 0.000</td> <td>    3.374</td> <td>    3.721</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.37</td> <th>  Jarque-Bera (JB):  </th> <td>225.37</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.54</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.78</td> <th>  Skew:              </th>  <td>-0.02</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>4.43</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 8.37e+17. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   No. Observations:                 2640\n",
       "Model:               SARIMAX(1, 0, 1)   Log Likelihood               -5409.140\n",
       "Date:                Wed, 10 Aug 2022   AIC                          10924.280\n",
       "Time:                        15:06:45   BIC                          11235.802\n",
       "Sample:                    01-08-2010   HQIC                         11037.069\n",
       "                         - 03-31-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "===============================================================================================\n",
       "                                  coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------------\n",
       "happiness_Log                   6.8180     10.671      0.639      0.523     -14.097      27.733\n",
       "bittersweet_Log                 9.9593     10.027      0.993      0.321      -9.693      29.612\n",
       "disgust_Log                    11.2544      7.468      1.507      0.132      -3.382      25.891\n",
       "disagreement_Log               -5.6049      1.962     -2.857      0.004      -9.450      -1.760\n",
       "anxiety_Log                    16.0652      6.167      2.605      0.009       3.979      28.152\n",
       "gloominess_Log                 -5.4296      2.824     -1.923      0.054     -10.964       0.105\n",
       "distress_Log                    7.0401      2.438      2.888      0.004       2.263      11.818\n",
       "violent_Log                     4.7138      1.672      2.819      0.005       1.436       7.991\n",
       "unexpectedness_Log             20.7752      6.172      3.366      0.001       8.679      32.871\n",
       "confusion_Log                  13.1904      4.467      2.953      0.003       4.434      21.946\n",
       "buyVsSell_sqrt                 28.6883      8.487      3.380      0.001      12.053      45.323\n",
       "pricePrediction_Log            36.2900      5.705      6.361      0.000      25.107      47.472\n",
       "volatile_Log                    3.6858      2.646      1.393      0.164      -1.501       8.873\n",
       "productionCommodity_Log        -1.1640      4.238     -0.275      0.784      -9.471       7.143\n",
       "regulatory_Log                 57.5013     11.118      5.172      0.000      35.711      79.292\n",
       "supplyDemandBalance_Log        -5.5829      0.720     -7.758      0.000      -6.993      -4.173\n",
       "exploration_Log               -61.1171     21.112     -2.895      0.004    -102.495     -19.739\n",
       "accidents_Log                 -37.6504     16.131     -2.334      0.020     -69.266      -6.035\n",
       "supplyDemandBalanceForecast    -8.5816      3.394     -2.529      0.011     -15.233      -1.930\n",
       "consumptionCommodity           -6.4691      7.063     -0.916      0.360     -20.312       7.374\n",
       "priceTrend                     15.8746      1.937      8.195      0.000      12.078      19.671\n",
       "buyVsSellForecast             -21.5008     12.464     -1.725      0.085     -45.931       2.929\n",
       "feelingsVsTruth                -0.1484      0.539     -0.275      0.783      -1.205       0.909\n",
       "timeliness                     -9.1035      2.099     -4.336      0.000     -13.218      -4.989\n",
       "confidence                    -10.5887      6.829     -1.550      0.121     -23.974       2.796\n",
       "popularity                  -1.733e-06   2.19e-05     -0.079      0.937   -4.46e-05    4.11e-05\n",
       "general_sentiment               3.7484      0.637      5.887      0.000       2.501       4.996\n",
       "positive_outlook               -4.7500      1.546     -3.073      0.002      -7.780      -1.720\n",
       "price_lag_7                     0.9818      0.008    116.649      0.000       0.965       0.998\n",
       "month-1                         4.2999      5.141      0.836      0.403      -5.776      14.376\n",
       "month-2                         5.5534      5.110      1.087      0.277      -4.461      15.568\n",
       "month-3                         3.9296      5.107      0.770      0.442      -6.079      13.938\n",
       "month-4                         4.8772      5.109      0.955      0.340      -5.135      14.890\n",
       "month-5                         3.3161      5.119      0.648      0.517      -6.717      13.349\n",
       "month-6                         3.8771      5.130      0.756      0.450      -6.178      13.933\n",
       "month-7                         4.5954      5.126      0.896      0.370      -5.452      14.642\n",
       "month-8                         4.5481      5.128      0.887      0.375      -5.503      14.599\n",
       "month-9                         4.3614      5.097      0.856      0.392      -5.629      14.352\n",
       "month-10                        4.0110      5.112      0.785      0.433      -6.007      14.029\n",
       "month-11                        4.0287      5.119      0.787      0.431      -6.004      14.062\n",
       "month-12                        4.7551      5.147      0.924      0.356      -5.333      14.843\n",
       "dayofweek-0                     9.1360     10.930      0.836      0.403     -12.287      30.559\n",
       "dayofweek-1                     9.2582     10.920      0.848      0.397     -12.144      30.660\n",
       "dayofweek-2                     9.6471     10.918      0.884      0.377     -11.751      31.045\n",
       "dayofweek-3                     9.4990     10.924      0.870      0.385     -11.912      30.910\n",
       "dayofweek-4                     9.2461     10.925      0.846      0.397     -12.167      30.660\n",
       "dayofweek-5                     3.6111      4.367      0.827      0.408      -4.947      12.170\n",
       "dayofweek-6                     1.7554      2.188      0.802      0.422      -2.533       6.044\n",
       "weekend                         5.3666      6.554      0.819      0.413      -7.478      18.211\n",
       "sunday                          1.7554      2.188      0.802      0.422      -2.533       6.044\n",
       "ar.L1                           0.8379      0.012     67.732      0.000       0.814       0.862\n",
       "ma.L1                          -0.0285      0.022     -1.281      0.200      -0.072       0.015\n",
       "sigma2                          3.5474      0.089     40.038      0.000       3.374       3.721\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.37   Jarque-Bera (JB):               225.37\n",
       "Prob(Q):                              0.54   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.78   Skew:                            -0.02\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         4.43\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 8.37e+17. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm #time-series analysis for python\n",
    "\n",
    "arimax = sm.tsa.statespace.SARIMAX(tra,order=(1,0,1),seasonal_order=(0,0,0,0),exog = exog_train, freq='D',\n",
    "                                  enforce_stationarity=False, enforce_invertibility=False,).fit()\n",
    "arimax.summary()\n",
    "#We can use SARIMAX model as ARIMAX when seasonal_order is (0,0,0,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From PMD Auto-Arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=8961.818, Time=6.91 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=8957.901, Time=7.40 sec\n",
      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=8959.783, Time=7.24 sec\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\ASADUL~1.QAM\\AppData\\Local\\Temp/ipykernel_19244/3524303681.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpmdarima\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m arimaxAuto = pm.auto_arima(tra, X=exog_train, start_p=1, start_q=1,\n\u001b[0m\u001b[0;32m      3\u001b[0m                       \u001b[0mtest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'adf'\u001b[0m\u001b[1;33m,\u001b[0m       \u001b[1;31m# use adftest to find optimal 'd'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m                       \u001b[0mmax_p\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_q\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;31m# maximum p and q\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m                       \u001b[0mm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m              \u001b[1;31m# frequency of series\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\pmdarima\\arima\\auto.py\u001b[0m in \u001b[0;36mauto_arima\u001b[1;34m(y, X, start_p, d, start_q, max_p, max_d, max_q, start_P, D, start_Q, max_P, max_D, max_Q, max_order, m, seasonal, stationary, information_criterion, alpha, test, seasonal_test, stepwise, n_jobs, start_params, trend, method, maxiter, offset_test_args, seasonal_test_args, suppress_warnings, error_action, trace, random, random_state, n_fits, return_valid_fits, out_of_sample_size, scoring, scoring_args, with_intercept, sarimax_kwargs, **fit_args)\u001b[0m\n\u001b[0;32m    715\u001b[0m         )\n\u001b[0;32m    716\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 717\u001b[1;33m     \u001b[0msorted_res\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msearch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    718\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_return_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msorted_res\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_valid_fits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    719\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\pmdarima\\arima\\_auto_solvers.py\u001b[0m in \u001b[0;36msolve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    303\u001b[0m             \u001b[0m_q\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmax_q\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    304\u001b[0m             \u001b[0m_Q\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mmax_Q\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 305\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_q\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mD\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_Q\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    306\u001b[0m                 \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mP\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    307\u001b[0m                 \u001b[0mQ\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_Q\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\pmdarima\\arima\\_auto_solvers.py\u001b[0m in \u001b[0;36m_do_fit\u001b[1;34m(self, order, seasonal_order, constant)\u001b[0m\n\u001b[0;32m    231\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mk\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 233\u001b[1;33m             fit, fit_time, new_ic = self._fit_arima(\n\u001b[0m\u001b[0;32m    234\u001b[0m                 \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    235\u001b[0m                 \u001b[0mseasonal_order\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseasonal_order\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\pmdarima\\arima\\_auto_solvers.py\u001b[0m in \u001b[0;36m_fit_candidate_model\u001b[1;34m(y, X, order, seasonal_order, start_params, trend, method, maxiter, fit_params, suppress_warnings, trace, error_action, out_of_sample_size, scoring, scoring_args, with_intercept, information_criterion, **kwargs)\u001b[0m\n\u001b[0;32m    504\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    505\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 506\u001b[1;33m         \u001b[0mfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    507\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    508\u001b[0m     \u001b[1;31m# for non-stationarity errors or singular matrices, return None\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\pmdarima\\arima\\arima.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, y, X, **fit_args)\u001b[0m\n\u001b[0;32m    595\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    596\u001b[0m         \u001b[1;31m# Internal call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 597\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    598\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    599\u001b[0m         \u001b[1;31m# now make a forecast if we're validating to compute the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\pmdarima\\arima\\arima.py\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, y, X, **fit_args)\u001b[0m\n\u001b[0;32m    516\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcatch_warnings\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    517\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ignore'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 518\u001b[1;33m                 \u001b[0mfit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marima_res_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_fit_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    519\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    520\u001b[0m             \u001b[0mfit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marima_res_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_fit_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\pmdarima\\arima\\arima.py\u001b[0m in \u001b[0;36m_fit_wrapper\u001b[1;34m()\u001b[0m\n\u001b[0;32m    506\u001b[0m             \u001b[0mdisp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit_args\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"disp\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    507\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 508\u001b[1;33m             return arima, arima.fit(start_params=start_params,\n\u001b[0m\u001b[0;32m    509\u001b[0m                                     \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m                                     \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_maxiter\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\statespace\\mlemodel.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, transformed, includes_fixed, cov_type, cov_kwds, method, maxiter, full_output, disp, callback, return_params, optim_score, optim_complex_step, optim_hessian, flags, low_memory, **kwargs)\u001b[0m\n\u001b[0;32m    688\u001b[0m                 \u001b[0mflags\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'hessian_method'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moptim_hessian\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    689\u001b[0m             \u001b[0mfargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 690\u001b[1;33m             mlefit = super(MLEModel, self).fit(start_params, method=method,\n\u001b[0m\u001b[0;32m    691\u001b[0m                                                \u001b[0mfargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfargs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    692\u001b[0m                                                \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, **kwargs)\u001b[0m\n\u001b[0;32m    517\u001b[0m         \u001b[0mwarn_convergence\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'warn_convergence'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m         \u001b[0moptimizer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mOptimizer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 519\u001b[1;33m         xopt, retvals, optim_settings = optimizer._fit(f, score, start_params,\n\u001b[0m\u001b[0;32m    520\u001b[0m                                                        \u001b[0mfargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    521\u001b[0m                                                        \u001b[0mhessian\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhess\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\optimizer.py\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, objective, gradient, start_params, fargs, kwargs, hessian, method, maxiter, full_output, disp, callback, retall)\u001b[0m\n\u001b[0;32m    222\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m         \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit_funcs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 224\u001b[1;33m         xopt, retvals = func(objective, gradient, start_params, fargs, kwargs,\n\u001b[0m\u001b[0;32m    225\u001b[0m                             \u001b[0mdisp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdisp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    226\u001b[0m                             \u001b[0mretall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mretall\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\optimizer.py\u001b[0m in \u001b[0;36m_fit_lbfgs\u001b[1;34m(f, score, start_params, fargs, kwargs, disp, maxiter, callback, retall, full_output, hess)\u001b[0m\n\u001b[0;32m    627\u001b[0m         \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    628\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 629\u001b[1;33m     retvals = optimize.fmin_l_bfgs_b(func, start_params, maxiter=maxiter,\n\u001b[0m\u001b[0;32m    630\u001b[0m                                      \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfargs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    631\u001b[0m                                      \u001b[0mbounds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbounds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdisp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdisp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\lbfgsb.py\u001b[0m in \u001b[0;36mfmin_l_bfgs_b\u001b[1;34m(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)\u001b[0m\n\u001b[0;32m    195\u001b[0m             'maxls': maxls}\n\u001b[0;32m    196\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 197\u001b[1;33m     res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,\n\u001b[0m\u001b[0;32m    198\u001b[0m                            **opts)\n\u001b[0;32m    199\u001b[0m     d = {'grad': res['jac'],\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\lbfgsb.py\u001b[0m in \u001b[0;36m_minimize_lbfgsb\u001b[1;34m(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)\u001b[0m\n\u001b[0;32m    358\u001b[0m             \u001b[1;31m# until the completion of the current minimization iteration.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    359\u001b[0m             \u001b[1;31m# Overwrite f and g:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 360\u001b[1;33m             \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc_and_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    361\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mtask_str\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mb'NEW_X'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    362\u001b[0m             \u001b[1;31m# new iteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_differentiable_functions.py\u001b[0m in \u001b[0;36mfun_and_grad\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    266\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_x_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    267\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_fun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    269\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    270\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_differentiable_functions.py\u001b[0m in \u001b[0;36m_update_grad\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    236\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_update_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    237\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg_updated\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 238\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_grad_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    239\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg_updated\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_differentiable_functions.py\u001b[0m in \u001b[0;36mupdate_grad\u001b[1;34m()\u001b[0m\n\u001b[0;32m    153\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_fun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mngev\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m                 self.g = approx_derivative(fun_wrapped, self.x, f0=self.f,\n\u001b[0m\u001b[0;32m    156\u001b[0m                                            **finite_diff_options)\n\u001b[0;32m    157\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_numdiff.py\u001b[0m in \u001b[0;36mapprox_derivative\u001b[1;34m(fun, x0, method, rel_step, abs_step, f0, bounds, sparsity, as_linear_operator, args, kwargs)\u001b[0m\n\u001b[0;32m    484\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    485\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msparsity\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 486\u001b[1;33m             return _dense_difference(fun_wrapped, x0, f0, h,\n\u001b[0m\u001b[0;32m    487\u001b[0m                                      use_one_sided, method)\n\u001b[0;32m    488\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_numdiff.py\u001b[0m in \u001b[0;36m_dense_difference\u001b[1;34m(fun, x0, f0, h, use_one_sided, method)\u001b[0m\n\u001b[0;32m    555\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx0\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mh_vecs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    556\u001b[0m             \u001b[0mdx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mx0\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m  \u001b[1;31m# Recompute dx as exactly representable number.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 557\u001b[1;33m             \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mf0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    558\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'3-point'\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0muse_one_sided\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m             \u001b[0mx1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx0\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mh_vecs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_numdiff.py\u001b[0m in \u001b[0;36mfun_wrapped\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    436\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfun_wrapped\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 437\u001b[1;33m         \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    438\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    439\u001b[0m             raise RuntimeError(\"`fun` return value has \"\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_differentiable_functions.py\u001b[0m in \u001b[0;36mfun_wrapped\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m    132\u001b[0m             \u001b[1;31m# Overwriting results in undefined behaviour because\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m             \u001b[1;31m# fun(self.x) will change self.x, with the two no longer linked.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 134\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    135\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mupdate_fun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(params, *args)\u001b[0m\n\u001b[0;32m    499\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    500\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 501\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloglike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mnobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    502\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    503\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'newton'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\statespace\\mlemodel.py\u001b[0m in \u001b[0;36mloglike\u001b[1;34m(self, params, *args, **kwargs)\u001b[0m\n\u001b[0;32m    923\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'inversion_method'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mINVERT_UNIVARIATE\u001b[0m \u001b[1;33m|\u001b[0m \u001b[0mSOLVE_LU\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    924\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 925\u001b[1;33m         \u001b[0mloglike\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mssm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloglike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcomplex_step\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcomplex_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    926\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    927\u001b[0m         \u001b[1;31m# Koopman, Shephard, and Doornik recommend maximizing the average\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\statespace\\kalman_filter.py\u001b[0m in \u001b[0;36mloglike\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    981\u001b[0m         kwargs.setdefault('conserve_memory',\n\u001b[0;32m    982\u001b[0m                           MEMORY_CONSERVE ^ MEMORY_NO_LIKELIHOOD)\n\u001b[1;32m--> 983\u001b[1;33m         \u001b[0mkfilter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    984\u001b[0m         loglikelihood_burn = kwargs.get('loglikelihood_burn',\n\u001b[0;32m    985\u001b[0m                                         self.loglikelihood_burn)\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\statespace\\kalman_filter.py\u001b[0m in \u001b[0;36m_filter\u001b[1;34m(self, filter_method, inversion_method, stability_method, conserve_memory, filter_timing, tolerance, loglikelihood_burn, complex_step)\u001b[0m\n\u001b[0;32m    893\u001b[0m         \u001b[1;31m# Initialize the filter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m         prefix, dtype, create_filter, create_statespace = (\n\u001b[1;32m--> 895\u001b[1;33m             self._initialize_filter(\n\u001b[0m\u001b[0;32m    896\u001b[0m                 \u001b[0mfilter_method\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minversion_method\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstability_method\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    897\u001b[0m                 \u001b[0mconserve_memory\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilter_timing\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloglikelihood_burn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\statespace\\kalman_filter.py\u001b[0m in \u001b[0;36m_initialize_filter\u001b[1;34m(self, filter_method, inversion_method, stability_method, conserve_memory, tolerance, filter_timing, loglikelihood_burn)\u001b[0m\n\u001b[0;32m    460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    461\u001b[0m         \u001b[1;31m# Initialize the representation matrices\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m         \u001b[0mprefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcreate_statespace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initialize_representation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    464\u001b[0m         \u001b[1;31m# Determine if we need to (re-)create the filter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\asadullahqamar.qamar\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\statespace\\representation.py\u001b[0m in \u001b[0;36m_initialize_representation\u001b[1;34m(self, prefix)\u001b[0m\n\u001b[0;32m    930\u001b[0m                     \u001b[0mnew\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    931\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mexisting\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mnew\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 932\u001b[1;33m                         \u001b[0mexisting\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    933\u001b[0m                     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    934\u001b[0m                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_representations\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mprefix\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# import pmdarima as pm\n",
    "# arimaxAuto = pm.auto_arima(tra, X=exog_train, start_p=1, start_q=1,\n",
    "#                       test='adf',       # use adftest to find optimal 'd'\n",
    "#                       max_p=3, max_q=3, # maximum p and q\n",
    "#                       m=7,              # frequency of series\n",
    "#                       d=1,           # let model determine 'd'\n",
    "#                       seasonal=False,   # No Seasonality\n",
    "#                       start_P=0, \n",
    "#                       D=0, \n",
    "#                       trace=True,\n",
    "#                       error_action='ignore',  \n",
    "#                       suppress_warnings=True, \n",
    "#                       stepwise=True)\n",
    "# arimaxAuto.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "tra.index = tra.index.to_timestamp(freq='D')\n",
    "exog_train.index = exog_train.index.to_timestamp(freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>price</td>      <th>  No. Observations:  </th>   <td>2640</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)</td> <th>  Log Likelihood     </th> <td>-4424.440</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 10 Aug 2022</td> <th>  AIC                </th> <td>8954.880</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:08:21</td>     <th>  BIC                </th> <td>9266.382</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-08-2010</td>    <th>  HQIC               </th> <td>9067.664</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 03-31-2017</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</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>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>happiness_Log</th>               <td>   -3.5315</td> <td>    7.303</td> <td>   -0.484</td> <td> 0.629</td> <td>  -17.845</td> <td>   10.782</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bittersweet_Log</th>             <td>   -2.5417</td> <td>    6.774</td> <td>   -0.375</td> <td> 0.708</td> <td>  -15.819</td> <td>   10.736</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>disgust_Log</th>                 <td>    8.3547</td> <td>    4.987</td> <td>    1.675</td> <td> 0.094</td> <td>   -1.419</td> <td>   18.128</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>disagreement_Log</th>            <td>   -0.3402</td> <td>    1.351</td> <td>   -0.252</td> <td> 0.801</td> <td>   -2.988</td> <td>    2.307</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>anxiety_Log</th>                 <td>    5.1473</td> <td>    3.918</td> <td>    1.314</td> <td> 0.189</td> <td>   -2.533</td> <td>   12.827</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gloominess_Log</th>              <td>   -0.5449</td> <td>    1.929</td> <td>   -0.282</td> <td> 0.778</td> <td>   -4.326</td> <td>    3.236</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distress_Log</th>                <td>    1.4476</td> <td>    1.653</td> <td>    0.875</td> <td> 0.381</td> <td>   -1.793</td> <td>    4.688</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>violent_Log</th>                 <td>   -0.4051</td> <td>    1.104</td> <td>   -0.367</td> <td> 0.714</td> <td>   -2.568</td> <td>    1.758</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>unexpectedness_Log</th>          <td>    8.7172</td> <td>    3.971</td> <td>    2.195</td> <td> 0.028</td> <td>    0.934</td> <td>   16.500</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>confusion_Log</th>               <td>    3.6748</td> <td>    2.936</td> <td>    1.252</td> <td> 0.211</td> <td>   -2.080</td> <td>    9.429</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>buyVsSell_sqrt</th>              <td>    7.6149</td> <td>    6.205</td> <td>    1.227</td> <td> 0.220</td> <td>   -4.547</td> <td>   19.777</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pricePrediction_Log</th>         <td>    0.0968</td> <td>    4.718</td> <td>    0.021</td> <td> 0.984</td> <td>   -9.150</td> <td>    9.344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>volatile_Log</th>                <td>   -0.3289</td> <td>    1.779</td> <td>   -0.185</td> <td> 0.853</td> <td>   -3.816</td> <td>    3.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>productionCommodity_Log</th>     <td>    1.0442</td> <td>    2.931</td> <td>    0.356</td> <td> 0.722</td> <td>   -4.701</td> <td>    6.790</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regulatory_Log</th>              <td>    3.9093</td> <td>   12.315</td> <td>    0.317</td> <td> 0.751</td> <td>  -20.227</td> <td>   28.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>supplyDemandBalance_Log</th>     <td>   -0.8786</td> <td>    0.450</td> <td>   -1.954</td> <td> 0.051</td> <td>   -1.760</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>exploration_Log</th>             <td>  -32.0177</td> <td>   16.129</td> <td>   -1.985</td> <td> 0.047</td> <td>  -63.631</td> <td>   -0.405</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>accidents_Log</th>               <td>   17.1822</td> <td>   10.841</td> <td>    1.585</td> <td> 0.113</td> <td>   -4.066</td> <td>   38.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>supplyDemandBalanceForecast</th> <td>   -0.1397</td> <td>    2.206</td> <td>   -0.063</td> <td> 0.949</td> <td>   -4.463</td> <td>    4.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>consumptionCommodity</th>        <td>   -1.9579</td> <td>    4.742</td> <td>   -0.413</td> <td> 0.680</td> <td>  -11.252</td> <td>    7.336</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>priceTrend</th>                  <td>    1.8262</td> <td>    1.392</td> <td>    1.312</td> <td> 0.190</td> <td>   -0.903</td> <td>    4.555</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>buyVsSellForecast</th>           <td>   -6.6091</td> <td>    8.410</td> <td>   -0.786</td> <td> 0.432</td> <td>  -23.092</td> <td>    9.873</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>feelingsVsTruth</th>             <td>   -0.1426</td> <td>    0.354</td> <td>   -0.403</td> <td> 0.687</td> <td>   -0.836</td> <td>    0.551</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>timeliness</th>                  <td>   -3.1638</td> <td>    1.487</td> <td>   -2.128</td> <td> 0.033</td> <td>   -6.078</td> <td>   -0.250</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>confidence</th>                  <td>    3.8142</td> <td>    4.944</td> <td>    0.772</td> <td> 0.440</td> <td>   -5.875</td> <td>   13.503</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popularity</th>                  <td>-2.093e-05</td> <td> 1.36e-05</td> <td>   -1.537</td> <td> 0.124</td> <td>-4.76e-05</td> <td> 5.76e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>general_sentiment</th>           <td>    1.0904</td> <td>    0.423</td> <td>    2.575</td> <td> 0.010</td> <td>    0.260</td> <td>    1.920</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>positive_outlook</th>            <td>   -1.4684</td> <td>    1.067</td> <td>   -1.376</td> <td> 0.169</td> <td>   -3.560</td> <td>    0.623</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>price_lag_7</th>                 <td>    0.0496</td> <td>    0.016</td> <td>    3.028</td> <td> 0.002</td> <td>    0.017</td> <td>    0.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-1</th>                     <td>   -0.4420</td> <td>    1.562</td> <td>   -0.283</td> <td> 0.777</td> <td>   -3.503</td> <td>    2.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-2</th>                     <td>   -0.0206</td> <td>    1.514</td> <td>   -0.014</td> <td> 0.989</td> <td>   -2.988</td> <td>    2.947</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-3</th>                     <td>    0.1685</td> <td>    1.500</td> <td>    0.112</td> <td> 0.911</td> <td>   -2.771</td> <td>    3.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-4</th>                     <td>    0.4927</td> <td>    1.488</td> <td>    0.331</td> <td> 0.741</td> <td>   -2.423</td> <td>    3.409</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-5</th>                     <td>    0.3568</td> <td>    0.996</td> <td>    0.358</td> <td> 0.720</td> <td>   -1.595</td> <td>    2.309</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-6</th>                     <td>   -0.5099</td> <td>    0.915</td> <td>   -0.557</td> <td> 0.577</td> <td>   -2.303</td> <td>    1.283</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-7</th>                     <td>   -0.4522</td> <td>    0.894</td> <td>   -0.506</td> <td> 0.613</td> <td>   -2.205</td> <td>    1.300</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-8</th>                     <td>    0.2186</td> <td>    0.881</td> <td>    0.248</td> <td> 0.804</td> <td>   -1.507</td> <td>    1.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-9</th>                     <td>    0.4106</td> <td>    0.890</td> <td>    0.461</td> <td> 0.645</td> <td>   -1.334</td> <td>    2.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-10</th>                    <td>    0.3206</td> <td>    0.917</td> <td>    0.350</td> <td> 0.727</td> <td>   -1.477</td> <td>    2.118</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-11</th>                    <td>   -0.1614</td> <td>    0.983</td> <td>   -0.164</td> <td> 0.870</td> <td>   -2.088</td> <td>    1.766</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-12</th>                    <td>   -0.3817</td> <td>    1.015</td> <td>   -0.376</td> <td> 0.707</td> <td>   -2.370</td> <td>    1.607</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-0</th>                 <td>    0.0223</td> <td>    0.068</td> <td>    0.326</td> <td> 0.744</td> <td>   -0.112</td> <td>    0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-1</th>                 <td>   -0.1428</td> <td>    0.050</td> <td>   -2.859</td> <td> 0.004</td> <td>   -0.241</td> <td>   -0.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-2</th>                 <td>   -0.0801</td> <td>    0.051</td> <td>   -1.566</td> <td> 0.117</td> <td>   -0.180</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-3</th>                 <td>    0.0625</td> <td>    0.055</td> <td>    1.137</td> <td> 0.256</td> <td>   -0.045</td> <td>    0.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-4</th>                 <td>    0.1021</td> <td>    0.068</td> <td>    1.501</td> <td> 0.133</td> <td>   -0.031</td> <td>    0.235</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-5</th>                 <td>    0.0242</td> <td>    0.162</td> <td>    0.149</td> <td> 0.882</td> <td>   -0.294</td> <td>    0.343</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-6</th>                 <td>    0.0118</td> <td>    0.068</td> <td>    0.174</td> <td> 0.862</td> <td>   -0.121</td> <td>    0.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weekend</th>                     <td>    0.0360</td> <td>    0.110</td> <td>    0.328</td> <td> 0.743</td> <td>   -0.179</td> <td>    0.251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sunday</th>                      <td>    0.0118</td> <td>    0.068</td> <td>    0.174</td> <td> 0.862</td> <td>   -0.121</td> <td>    0.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>                       <td>    0.3029</td> <td>    2.128</td> <td>    0.142</td> <td> 0.887</td> <td>   -3.869</td> <td>    4.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>                       <td>   -0.2962</td> <td>    2.133</td> <td>   -0.139</td> <td> 0.890</td> <td>   -4.477</td> <td>    3.884</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>                      <td>    1.6788</td> <td>    0.027</td> <td>   61.810</td> <td> 0.000</td> <td>    1.626</td> <td>    1.732</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>4508.32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>1.00</td> <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.71</td> <th>  Skew:              </th>  <td>-0.42</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>9.35</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 2.12e+20. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   No. Observations:                 2640\n",
       "Model:               SARIMAX(1, 1, 1)   Log Likelihood               -4424.440\n",
       "Date:                Wed, 10 Aug 2022   AIC                           8954.880\n",
       "Time:                        15:08:21   BIC                           9266.382\n",
       "Sample:                    01-08-2010   HQIC                          9067.664\n",
       "                         - 03-31-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "===============================================================================================\n",
       "                                  coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------------\n",
       "happiness_Log                  -3.5315      7.303     -0.484      0.629     -17.845      10.782\n",
       "bittersweet_Log                -2.5417      6.774     -0.375      0.708     -15.819      10.736\n",
       "disgust_Log                     8.3547      4.987      1.675      0.094      -1.419      18.128\n",
       "disagreement_Log               -0.3402      1.351     -0.252      0.801      -2.988       2.307\n",
       "anxiety_Log                     5.1473      3.918      1.314      0.189      -2.533      12.827\n",
       "gloominess_Log                 -0.5449      1.929     -0.282      0.778      -4.326       3.236\n",
       "distress_Log                    1.4476      1.653      0.875      0.381      -1.793       4.688\n",
       "violent_Log                    -0.4051      1.104     -0.367      0.714      -2.568       1.758\n",
       "unexpectedness_Log              8.7172      3.971      2.195      0.028       0.934      16.500\n",
       "confusion_Log                   3.6748      2.936      1.252      0.211      -2.080       9.429\n",
       "buyVsSell_sqrt                  7.6149      6.205      1.227      0.220      -4.547      19.777\n",
       "pricePrediction_Log             0.0968      4.718      0.021      0.984      -9.150       9.344\n",
       "volatile_Log                   -0.3289      1.779     -0.185      0.853      -3.816       3.158\n",
       "productionCommodity_Log         1.0442      2.931      0.356      0.722      -4.701       6.790\n",
       "regulatory_Log                  3.9093     12.315      0.317      0.751     -20.227      28.045\n",
       "supplyDemandBalance_Log        -0.8786      0.450     -1.954      0.051      -1.760       0.003\n",
       "exploration_Log               -32.0177     16.129     -1.985      0.047     -63.631      -0.405\n",
       "accidents_Log                  17.1822     10.841      1.585      0.113      -4.066      38.431\n",
       "supplyDemandBalanceForecast    -0.1397      2.206     -0.063      0.949      -4.463       4.184\n",
       "consumptionCommodity           -1.9579      4.742     -0.413      0.680     -11.252       7.336\n",
       "priceTrend                      1.8262      1.392      1.312      0.190      -0.903       4.555\n",
       "buyVsSellForecast              -6.6091      8.410     -0.786      0.432     -23.092       9.873\n",
       "feelingsVsTruth                -0.1426      0.354     -0.403      0.687      -0.836       0.551\n",
       "timeliness                     -3.1638      1.487     -2.128      0.033      -6.078      -0.250\n",
       "confidence                      3.8142      4.944      0.772      0.440      -5.875      13.503\n",
       "popularity                  -2.093e-05   1.36e-05     -1.537      0.124   -4.76e-05    5.76e-06\n",
       "general_sentiment               1.0904      0.423      2.575      0.010       0.260       1.920\n",
       "positive_outlook               -1.4684      1.067     -1.376      0.169      -3.560       0.623\n",
       "price_lag_7                     0.0496      0.016      3.028      0.002       0.017       0.082\n",
       "month-1                        -0.4420      1.562     -0.283      0.777      -3.503       2.619\n",
       "month-2                        -0.0206      1.514     -0.014      0.989      -2.988       2.947\n",
       "month-3                         0.1685      1.500      0.112      0.911      -2.771       3.108\n",
       "month-4                         0.4927      1.488      0.331      0.741      -2.423       3.409\n",
       "month-5                         0.3568      0.996      0.358      0.720      -1.595       2.309\n",
       "month-6                        -0.5099      0.915     -0.557      0.577      -2.303       1.283\n",
       "month-7                        -0.4522      0.894     -0.506      0.613      -2.205       1.300\n",
       "month-8                         0.2186      0.881      0.248      0.804      -1.507       1.945\n",
       "month-9                         0.4106      0.890      0.461      0.645      -1.334       2.155\n",
       "month-10                        0.3206      0.917      0.350      0.727      -1.477       2.118\n",
       "month-11                       -0.1614      0.983     -0.164      0.870      -2.088       1.766\n",
       "month-12                       -0.3817      1.015     -0.376      0.707      -2.370       1.607\n",
       "dayofweek-0                     0.0223      0.068      0.326      0.744      -0.112       0.156\n",
       "dayofweek-1                    -0.1428      0.050     -2.859      0.004      -0.241      -0.045\n",
       "dayofweek-2                    -0.0801      0.051     -1.566      0.117      -0.180       0.020\n",
       "dayofweek-3                     0.0625      0.055      1.137      0.256      -0.045       0.170\n",
       "dayofweek-4                     0.1021      0.068      1.501      0.133      -0.031       0.235\n",
       "dayofweek-5                     0.0242      0.162      0.149      0.882      -0.294       0.343\n",
       "dayofweek-6                     0.0118      0.068      0.174      0.862      -0.121       0.145\n",
       "weekend                         0.0360      0.110      0.328      0.743      -0.179       0.251\n",
       "sunday                          0.0118      0.068      0.174      0.862      -0.121       0.145\n",
       "ar.L1                           0.3029      2.128      0.142      0.887      -3.869       4.474\n",
       "ma.L1                          -0.2962      2.133     -0.139      0.890      -4.477       3.884\n",
       "sigma2                          1.6788      0.027     61.810      0.000       1.626       1.732\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):              4508.32\n",
       "Prob(Q):                              1.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.71   Skew:                            -0.42\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         9.35\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 2.12e+20. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm #time-series analysis for python\n",
    "\n",
    "arimaxAuto = sm.tsa.statespace.SARIMAX(tra,order=(1,1,1),seasonal_order=(0,0,0,0),exog = exog_train, freq='D',\n",
    "                                  enforce_stationarity=False, enforce_invertibility=False,).fit()\n",
    "arimaxAuto.summary()\n",
    "#We can use SARIMAX model as ARIMAX when seasonal_order is (0,0,0,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the Price to Price_Log So That The Multiplicative Variable is More Compatible with the Additive ARIMAX Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "traLog = np.log(tra)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From PMD Auto-Arima on Price_Log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=-14534.090, Time=8.85 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=-14538.239, Time=6.69 sec\n",
      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=-14536.424, Time=6.88 sec\n",
      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=-14536.431, Time=8.33 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=3612.370, Time=4.15 sec\n",
      "\n",
      "Best model:  ARIMA(0,1,0)(0,0,0)[0] intercept\n",
      "Total fit time: 34.922 seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>2640</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 0)</td> <th>  Log Likelihood     </th>  <td>7321.119</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 10 Aug 2022</td> <th>  AIC                </th> <td>-14538.239</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:10:36</td>     <th>  BIC                </th> <td>-14232.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-08-2010</td>    <th>  HQIC               </th> <td>-14427.573</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 03-31-2017</td>   <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</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>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>                   <td>   -0.0001</td> <td>    0.001</td> <td>   -0.104</td> <td> 0.917</td> <td>   -0.002</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>happiness_Log</th>               <td>   -0.0169</td> <td>    0.078</td> <td>   -0.217</td> <td> 0.828</td> <td>   -0.170</td> <td>    0.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bittersweet_Log</th>             <td>   -0.0469</td> <td>    0.077</td> <td>   -0.609</td> <td> 0.543</td> <td>   -0.198</td> <td>    0.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>disgust_Log</th>                 <td>    0.0909</td> <td>    0.060</td> <td>    1.516</td> <td> 0.129</td> <td>   -0.027</td> <td>    0.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>disagreement_Log</th>            <td>   -0.0076</td> <td>    0.015</td> <td>   -0.508</td> <td> 0.611</td> <td>   -0.037</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>anxiety_Log</th>                 <td>    0.0404</td> <td>    0.046</td> <td>    0.876</td> <td> 0.381</td> <td>   -0.050</td> <td>    0.131</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gloominess_Log</th>              <td>   -0.0056</td> <td>    0.023</td> <td>   -0.244</td> <td> 0.807</td> <td>   -0.051</td> <td>    0.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distress_Log</th>                <td>    0.0147</td> <td>    0.018</td> <td>    0.804</td> <td> 0.422</td> <td>   -0.021</td> <td>    0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>violent_Log</th>                 <td>   -0.0034</td> <td>    0.013</td> <td>   -0.261</td> <td> 0.794</td> <td>   -0.029</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>unexpectedness_Log</th>          <td>    0.0948</td> <td>    0.046</td> <td>    2.043</td> <td> 0.041</td> <td>    0.004</td> <td>    0.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>confusion_Log</th>               <td>    0.0464</td> <td>    0.034</td> <td>    1.370</td> <td> 0.171</td> <td>   -0.020</td> <td>    0.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>buyVsSell_sqrt</th>              <td>    0.0780</td> <td>    0.071</td> <td>    1.100</td> <td> 0.271</td> <td>   -0.061</td> <td>    0.217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pricePrediction_Log</th>         <td>   -0.0172</td> <td>    0.054</td> <td>   -0.318</td> <td> 0.751</td> <td>   -0.123</td> <td>    0.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>volatile_Log</th>                <td>   -0.0064</td> <td>    0.022</td> <td>   -0.291</td> <td> 0.771</td> <td>   -0.049</td> <td>    0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>productionCommodity_Log</th>     <td>    0.0173</td> <td>    0.032</td> <td>    0.539</td> <td> 0.590</td> <td>   -0.046</td> <td>    0.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regulatory_Log</th>              <td>    0.0116</td> <td>    0.149</td> <td>    0.078</td> <td> 0.938</td> <td>   -0.280</td> <td>    0.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>supplyDemandBalance_Log</th>     <td>   -0.0076</td> <td>    0.005</td> <td>   -1.405</td> <td> 0.160</td> <td>   -0.018</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>exploration_Log</th>             <td>   -0.3286</td> <td>    0.192</td> <td>   -1.714</td> <td> 0.086</td> <td>   -0.704</td> <td>    0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>accidents_Log</th>               <td>    0.1832</td> <td>    0.146</td> <td>    1.254</td> <td> 0.210</td> <td>   -0.103</td> <td>    0.469</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>supplyDemandBalanceForecast</th> <td>   -0.0075</td> <td>    0.025</td> <td>   -0.303</td> <td> 0.762</td> <td>   -0.056</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>consumptionCommodity</th>        <td>   -0.0350</td> <td>    0.050</td> <td>   -0.700</td> <td> 0.484</td> <td>   -0.133</td> <td>    0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>priceTrend</th>                  <td>    0.0174</td> <td>    0.017</td> <td>    1.023</td> <td> 0.306</td> <td>   -0.016</td> <td>    0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>buyVsSellForecast</th>           <td>   -0.0840</td> <td>    0.098</td> <td>   -0.858</td> <td> 0.391</td> <td>   -0.276</td> <td>    0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>feelingsVsTruth</th>             <td>   -0.0013</td> <td>    0.004</td> <td>   -0.341</td> <td> 0.733</td> <td>   -0.009</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>timeliness</th>                  <td>   -0.0364</td> <td>    0.018</td> <td>   -2.064</td> <td> 0.039</td> <td>   -0.071</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>confidence</th>                  <td>    0.0491</td> <td>    0.051</td> <td>    0.956</td> <td> 0.339</td> <td>   -0.051</td> <td>    0.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popularity</th>                  <td>-1.883e-07</td> <td> 1.74e-07</td> <td>   -1.083</td> <td> 0.279</td> <td>-5.29e-07</td> <td> 1.52e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>general_sentiment</th>           <td>    0.0097</td> <td>    0.005</td> <td>    2.024</td> <td> 0.043</td> <td>    0.000</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>positive_outlook</th>            <td>   -0.0164</td> <td>    0.012</td> <td>   -1.371</td> <td> 0.170</td> <td>   -0.040</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>price_lag_7</th>                 <td>    0.0006</td> <td>    0.000</td> <td>    3.123</td> <td> 0.002</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-1</th>                     <td>   -0.0025</td> <td>    0.018</td> <td>   -0.134</td> <td> 0.894</td> <td>   -0.039</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-2</th>                     <td>    0.0009</td> <td>    0.018</td> <td>    0.048</td> <td> 0.962</td> <td>   -0.034</td> <td>    0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-3</th>                     <td>    0.0052</td> <td>    0.018</td> <td>    0.292</td> <td> 0.770</td> <td>   -0.030</td> <td>    0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-4</th>                     <td>    0.0061</td> <td>    0.018</td> <td>    0.344</td> <td> 0.730</td> <td>   -0.029</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-5</th>                     <td>    0.0040</td> <td>    0.012</td> <td>    0.333</td> <td> 0.739</td> <td>   -0.019</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-6</th>                     <td>   -0.0061</td> <td>    0.011</td> <td>   -0.562</td> <td> 0.574</td> <td>   -0.028</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-7</th>                     <td>   -0.0089</td> <td>    0.011</td> <td>   -0.828</td> <td> 0.407</td> <td>   -0.030</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-8</th>                     <td>   -0.0016</td> <td>    0.011</td> <td>   -0.157</td> <td> 0.876</td> <td>   -0.022</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-9</th>                     <td>    0.0015</td> <td>    0.011</td> <td>    0.142</td> <td> 0.887</td> <td>   -0.019</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-10</th>                    <td>    0.0032</td> <td>    0.011</td> <td>    0.294</td> <td> 0.769</td> <td>   -0.018</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-11</th>                    <td>   -0.0003</td> <td>    0.012</td> <td>   -0.025</td> <td> 0.980</td> <td>   -0.024</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-12</th>                    <td>   -0.0014</td> <td>    0.012</td> <td>   -0.117</td> <td> 0.907</td> <td>   -0.025</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-0</th>                 <td>    0.0006</td> <td>    0.002</td> <td>    0.293</td> <td> 0.769</td> <td>   -0.003</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-1</th>                 <td>   -0.0017</td> <td>    0.001</td> <td>   -1.682</td> <td> 0.092</td> <td>   -0.004</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-2</th>                 <td>   -0.0015</td> <td>    0.001</td> <td>   -2.402</td> <td> 0.016</td> <td>   -0.003</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-3</th>                 <td>    0.0006</td> <td>    0.001</td> <td>    0.447</td> <td> 0.655</td> <td>   -0.002</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-4</th>                 <td>    0.0014</td> <td>    0.002</td> <td>    0.560</td> <td> 0.575</td> <td>   -0.003</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-5</th>                 <td>    0.0004</td> <td>    0.002</td> <td>    0.181</td> <td> 0.856</td> <td>   -0.004</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-6</th>                 <td>    0.0002</td> <td>    0.001</td> <td>    0.172</td> <td> 0.863</td> <td>   -0.002</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weekend</th>                     <td>    0.0006</td> <td>    0.002</td> <td>    0.332</td> <td> 0.740</td> <td>   -0.003</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sunday</th>                      <td>    0.0002</td> <td>    0.001</td> <td>    0.172</td> <td> 0.863</td> <td>   -0.002</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>                      <td>    0.0002</td> <td> 3.58e-06</td> <td>   63.736</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.19</td> <th>  Jarque-Bera (JB):  </th> <td>4962.73</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.67</td> <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>2.26</td> <th>  Skew:              </th>  <td>-0.39</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>9.67</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 1.36e+19. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                 2640\n",
       "Model:               SARIMAX(0, 1, 0)   Log Likelihood                7321.119\n",
       "Date:                Wed, 10 Aug 2022   AIC                         -14538.239\n",
       "Time:                        15:10:36   BIC                         -14232.575\n",
       "Sample:                    01-08-2010   HQIC                        -14427.573\n",
       "                         - 03-31-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "===============================================================================================\n",
       "                                  coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------------\n",
       "intercept                      -0.0001      0.001     -0.104      0.917      -0.002       0.002\n",
       "happiness_Log                  -0.0169      0.078     -0.217      0.828      -0.170       0.136\n",
       "bittersweet_Log                -0.0469      0.077     -0.609      0.543      -0.198       0.104\n",
       "disgust_Log                     0.0909      0.060      1.516      0.129      -0.027       0.208\n",
       "disagreement_Log               -0.0076      0.015     -0.508      0.611      -0.037       0.022\n",
       "anxiety_Log                     0.0404      0.046      0.876      0.381      -0.050       0.131\n",
       "gloominess_Log                 -0.0056      0.023     -0.244      0.807      -0.051       0.039\n",
       "distress_Log                    0.0147      0.018      0.804      0.422      -0.021       0.051\n",
       "violent_Log                    -0.0034      0.013     -0.261      0.794      -0.029       0.022\n",
       "unexpectedness_Log              0.0948      0.046      2.043      0.041       0.004       0.186\n",
       "confusion_Log                   0.0464      0.034      1.370      0.171      -0.020       0.113\n",
       "buyVsSell_sqrt                  0.0780      0.071      1.100      0.271      -0.061       0.217\n",
       "pricePrediction_Log            -0.0172      0.054     -0.318      0.751      -0.123       0.089\n",
       "volatile_Log                   -0.0064      0.022     -0.291      0.771      -0.049       0.036\n",
       "productionCommodity_Log         0.0173      0.032      0.539      0.590      -0.046       0.080\n",
       "regulatory_Log                  0.0116      0.149      0.078      0.938      -0.280       0.303\n",
       "supplyDemandBalance_Log        -0.0076      0.005     -1.405      0.160      -0.018       0.003\n",
       "exploration_Log                -0.3286      0.192     -1.714      0.086      -0.704       0.047\n",
       "accidents_Log                   0.1832      0.146      1.254      0.210      -0.103       0.469\n",
       "supplyDemandBalanceForecast    -0.0075      0.025     -0.303      0.762      -0.056       0.041\n",
       "consumptionCommodity           -0.0350      0.050     -0.700      0.484      -0.133       0.063\n",
       "priceTrend                      0.0174      0.017      1.023      0.306      -0.016       0.051\n",
       "buyVsSellForecast              -0.0840      0.098     -0.858      0.391      -0.276       0.108\n",
       "feelingsVsTruth                -0.0013      0.004     -0.341      0.733      -0.009       0.006\n",
       "timeliness                     -0.0364      0.018     -2.064      0.039      -0.071      -0.002\n",
       "confidence                      0.0491      0.051      0.956      0.339      -0.051       0.150\n",
       "popularity                  -1.883e-07   1.74e-07     -1.083      0.279   -5.29e-07    1.52e-07\n",
       "general_sentiment               0.0097      0.005      2.024      0.043       0.000       0.019\n",
       "positive_outlook               -0.0164      0.012     -1.371      0.170      -0.040       0.007\n",
       "price_lag_7                     0.0006      0.000      3.123      0.002       0.000       0.001\n",
       "month-1                        -0.0025      0.018     -0.134      0.894      -0.039       0.034\n",
       "month-2                         0.0009      0.018      0.048      0.962      -0.034       0.036\n",
       "month-3                         0.0052      0.018      0.292      0.770      -0.030       0.040\n",
       "month-4                         0.0061      0.018      0.344      0.730      -0.029       0.041\n",
       "month-5                         0.0040      0.012      0.333      0.739      -0.019       0.027\n",
       "month-6                        -0.0061      0.011     -0.562      0.574      -0.028       0.015\n",
       "month-7                        -0.0089      0.011     -0.828      0.407      -0.030       0.012\n",
       "month-8                        -0.0016      0.011     -0.157      0.876      -0.022       0.019\n",
       "month-9                         0.0015      0.011      0.142      0.887      -0.019       0.022\n",
       "month-10                        0.0032      0.011      0.294      0.769      -0.018       0.025\n",
       "month-11                       -0.0003      0.012     -0.025      0.980      -0.024       0.023\n",
       "month-12                       -0.0014      0.012     -0.117      0.907      -0.025       0.023\n",
       "dayofweek-0                     0.0006      0.002      0.293      0.769      -0.003       0.005\n",
       "dayofweek-1                    -0.0017      0.001     -1.682      0.092      -0.004       0.000\n",
       "dayofweek-2                    -0.0015      0.001     -2.402      0.016      -0.003      -0.000\n",
       "dayofweek-3                     0.0006      0.001      0.447      0.655      -0.002       0.003\n",
       "dayofweek-4                     0.0014      0.002      0.560      0.575      -0.003       0.006\n",
       "dayofweek-5                     0.0004      0.002      0.181      0.856      -0.004       0.004\n",
       "dayofweek-6                     0.0002      0.001      0.172      0.863      -0.002       0.003\n",
       "weekend                         0.0006      0.002      0.332      0.740      -0.003       0.004\n",
       "sunday                          0.0002      0.001      0.172      0.863      -0.002       0.003\n",
       "sigma2                          0.0002   3.58e-06     63.736      0.000       0.000       0.000\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.19   Jarque-Bera (JB):              4962.73\n",
       "Prob(Q):                              0.67   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               2.26   Skew:                            -0.39\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         9.67\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 1.36e+19. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pmdarima as pm\n",
    "arimaxAutoLog = pm.auto_arima(traLog, X=exog_train, start_p=1, start_q=1,\n",
    "                      test='adf',       # use adftest to find optimal 'd'\n",
    "                      max_p=3, max_q=3, # maximum p and q\n",
    "                      m=7,              # frequency of series\n",
    "                      d=1,           # let model determine 'd'\n",
    "                      seasonal=False,   # No Seasonality\n",
    "                      start_P=0, \n",
    "                      D=0, \n",
    "                      trace=True,\n",
    "                      error_action='ignore',  \n",
    "                      suppress_warnings=True, \n",
    "                      stepwise=True)\n",
    "arimaxAutoLog.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>price</td>      <th>  No. Observations:  </th>    <td>2640</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)</td> <th>  Log Likelihood     </th>  <td>7314.543</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 10 Aug 2022</td> <th>  AIC                </th> <td>-14523.086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:18:48</td>     <th>  BIC                </th> <td>-14211.584</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-08-2010</td>    <th>  HQIC               </th> <td>-14410.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 03-31-2017</td>   <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</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>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>happiness_Log</th>               <td>   -0.0169</td> <td>    0.079</td> <td>   -0.214</td> <td> 0.830</td> <td>   -0.172</td> <td>    0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bittersweet_Log</th>             <td>   -0.0469</td> <td>    0.078</td> <td>   -0.604</td> <td> 0.546</td> <td>   -0.199</td> <td>    0.105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>disgust_Log</th>                 <td>    0.0909</td> <td>    0.060</td> <td>    1.505</td> <td> 0.132</td> <td>   -0.028</td> <td>    0.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>disagreement_Log</th>            <td>   -0.0076</td> <td>    0.015</td> <td>   -0.500</td> <td> 0.617</td> <td>   -0.038</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>anxiety_Log</th>                 <td>    0.0404</td> <td>    0.046</td> <td>    0.871</td> <td> 0.384</td> <td>   -0.050</td> <td>    0.131</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gloominess_Log</th>              <td>   -0.0056</td> <td>    0.023</td> <td>   -0.241</td> <td> 0.809</td> <td>   -0.051</td> <td>    0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distress_Log</th>                <td>    0.0147</td> <td>    0.019</td> <td>    0.796</td> <td> 0.426</td> <td>   -0.022</td> <td>    0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>violent_Log</th>                 <td>   -0.0034</td> <td>    0.013</td> <td>   -0.258</td> <td> 0.796</td> <td>   -0.029</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>unexpectedness_Log</th>          <td>    0.0948</td> <td>    0.047</td> <td>    2.025</td> <td> 0.043</td> <td>    0.003</td> <td>    0.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>confusion_Log</th>               <td>    0.0464</td> <td>    0.034</td> <td>    1.358</td> <td> 0.175</td> <td>   -0.021</td> <td>    0.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>buyVsSell_sqrt</th>              <td>    0.0780</td> <td>    0.072</td> <td>    1.084</td> <td> 0.278</td> <td>   -0.063</td> <td>    0.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pricePrediction_Log</th>         <td>   -0.0172</td> <td>    0.054</td> <td>   -0.315</td> <td> 0.753</td> <td>   -0.124</td> <td>    0.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>volatile_Log</th>                <td>   -0.0064</td> <td>    0.022</td> <td>   -0.290</td> <td> 0.772</td> <td>   -0.049</td> <td>    0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>productionCommodity_Log</th>     <td>    0.0173</td> <td>    0.032</td> <td>    0.536</td> <td> 0.592</td> <td>   -0.046</td> <td>    0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regulatory_Log</th>              <td>    0.0116</td> <td>    0.150</td> <td>    0.077</td> <td> 0.939</td> <td>   -0.283</td> <td>    0.306</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>supplyDemandBalance_Log</th>     <td>   -0.0076</td> <td>    0.005</td> <td>   -1.397</td> <td> 0.163</td> <td>   -0.018</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>exploration_Log</th>             <td>   -0.3286</td> <td>    0.193</td> <td>   -1.703</td> <td> 0.088</td> <td>   -0.707</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>accidents_Log</th>               <td>    0.1832</td> <td>    0.146</td> <td>    1.253</td> <td> 0.210</td> <td>   -0.103</td> <td>    0.470</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>supplyDemandBalanceForecast</th> <td>   -0.0075</td> <td>    0.025</td> <td>   -0.302</td> <td> 0.763</td> <td>   -0.056</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>consumptionCommodity</th>        <td>   -0.0350</td> <td>    0.050</td> <td>   -0.696</td> <td> 0.486</td> <td>   -0.133</td> <td>    0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>priceTrend</th>                  <td>    0.0174</td> <td>    0.017</td> <td>    1.018</td> <td> 0.309</td> <td>   -0.016</td> <td>    0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>buyVsSellForecast</th>           <td>   -0.0840</td> <td>    0.099</td> <td>   -0.852</td> <td> 0.394</td> <td>   -0.277</td> <td>    0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>feelingsVsTruth</th>             <td>   -0.0013</td> <td>    0.004</td> <td>   -0.337</td> <td> 0.736</td> <td>   -0.009</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>timeliness</th>                  <td>   -0.0364</td> <td>    0.018</td> <td>   -2.049</td> <td> 0.040</td> <td>   -0.071</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>confidence</th>                  <td>    0.0491</td> <td>    0.052</td> <td>    0.946</td> <td> 0.344</td> <td>   -0.053</td> <td>    0.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popularity</th>                  <td>-1.883e-07</td> <td> 1.74e-07</td> <td>   -1.081</td> <td> 0.279</td> <td> -5.3e-07</td> <td> 1.53e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>general_sentiment</th>           <td>    0.0097</td> <td>    0.005</td> <td>    2.011</td> <td> 0.044</td> <td>    0.000</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>positive_outlook</th>            <td>   -0.0164</td> <td>    0.012</td> <td>   -1.363</td> <td> 0.173</td> <td>   -0.040</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>price_lag_7</th>                 <td>    0.0006</td> <td>    0.000</td> <td>    3.107</td> <td> 0.002</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-1</th>                     <td>   -0.0025</td> <td>    0.019</td> <td>   -0.131</td> <td> 0.895</td> <td>   -0.039</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-2</th>                     <td>    0.0009</td> <td>    0.018</td> <td>    0.047</td> <td> 0.962</td> <td>   -0.035</td> <td>    0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-3</th>                     <td>    0.0052</td> <td>    0.018</td> <td>    0.287</td> <td> 0.774</td> <td>   -0.030</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-4</th>                     <td>    0.0061</td> <td>    0.018</td> <td>    0.340</td> <td> 0.734</td> <td>   -0.029</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-5</th>                     <td>    0.0040</td> <td>    0.012</td> <td>    0.332</td> <td> 0.740</td> <td>   -0.019</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-6</th>                     <td>   -0.0061</td> <td>    0.011</td> <td>   -0.557</td> <td> 0.577</td> <td>   -0.028</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-7</th>                     <td>   -0.0089</td> <td>    0.011</td> <td>   -0.821</td> <td> 0.412</td> <td>   -0.030</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-8</th>                     <td>   -0.0016</td> <td>    0.011</td> <td>   -0.155</td> <td> 0.877</td> <td>   -0.023</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-9</th>                     <td>    0.0015</td> <td>    0.011</td> <td>    0.140</td> <td> 0.889</td> <td>   -0.020</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-10</th>                    <td>    0.0032</td> <td>    0.011</td> <td>    0.290</td> <td> 0.772</td> <td>   -0.019</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-11</th>                    <td>   -0.0003</td> <td>    0.012</td> <td>   -0.025</td> <td> 0.980</td> <td>   -0.024</td> <td>    0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month-12</th>                    <td>   -0.0014</td> <td>    0.012</td> <td>   -0.115</td> <td> 0.909</td> <td>   -0.026</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-0</th>                 <td>    0.0006</td> <td>    0.001</td> <td>    0.750</td> <td> 0.453</td> <td>   -0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-1</th>                 <td>   -0.0017</td> <td>    0.001</td> <td>   -2.874</td> <td> 0.004</td> <td>   -0.003</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-2</th>                 <td>   -0.0015</td> <td>    0.001</td> <td>   -2.545</td> <td> 0.011</td> <td>   -0.003</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-3</th>                 <td>    0.0006</td> <td>    0.001</td> <td>    0.983</td> <td> 0.325</td> <td>   -0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-4</th>                 <td>    0.0014</td> <td>    0.001</td> <td>    1.688</td> <td> 0.091</td> <td>   -0.000</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-5</th>                 <td>    0.0004</td> <td>    0.002</td> <td>    0.180</td> <td> 0.857</td> <td>   -0.004</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek-6</th>                 <td>    0.0002</td> <td>    0.001</td> <td>    0.244</td> <td> 0.807</td> <td>   -0.002</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weekend</th>                     <td>    0.0006</td> <td>    0.001</td> <td>    0.427</td> <td> 0.669</td> <td>   -0.002</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sunday</th>                      <td>    0.0002</td> <td>    0.001</td> <td>    0.244</td> <td> 0.807</td> <td>   -0.002</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>                       <td>   -0.2509</td> <td>    1.663</td> <td>   -0.151</td> <td> 0.880</td> <td>   -3.511</td> <td>    3.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>                       <td>    0.2428</td> <td>    1.666</td> <td>    0.146</td> <td> 0.884</td> <td>   -3.022</td> <td>    3.508</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>                      <td>    0.0002</td> <td> 3.68e-06</td> <td>   62.049</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>4976.23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.98</td> <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>2.25</td> <th>  Skew:              </th>  <td>-0.39</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>9.68</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 9.93e+23. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   No. Observations:                 2640\n",
       "Model:               SARIMAX(1, 1, 1)   Log Likelihood                7314.543\n",
       "Date:                Wed, 10 Aug 2022   AIC                         -14523.086\n",
       "Time:                        15:18:48   BIC                         -14211.584\n",
       "Sample:                    01-08-2010   HQIC                        -14410.302\n",
       "                         - 03-31-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "===============================================================================================\n",
       "                                  coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------------\n",
       "happiness_Log                  -0.0169      0.079     -0.214      0.830      -0.172       0.138\n",
       "bittersweet_Log                -0.0469      0.078     -0.604      0.546      -0.199       0.105\n",
       "disgust_Log                     0.0909      0.060      1.505      0.132      -0.028       0.209\n",
       "disagreement_Log               -0.0076      0.015     -0.500      0.617      -0.038       0.022\n",
       "anxiety_Log                     0.0404      0.046      0.871      0.384      -0.050       0.131\n",
       "gloominess_Log                 -0.0056      0.023     -0.241      0.809      -0.051       0.040\n",
       "distress_Log                    0.0147      0.019      0.796      0.426      -0.022       0.051\n",
       "violent_Log                    -0.0034      0.013     -0.258      0.796      -0.029       0.022\n",
       "unexpectedness_Log              0.0948      0.047      2.025      0.043       0.003       0.187\n",
       "confusion_Log                   0.0464      0.034      1.358      0.175      -0.021       0.113\n",
       "buyVsSell_sqrt                  0.0780      0.072      1.084      0.278      -0.063       0.219\n",
       "pricePrediction_Log            -0.0172      0.054     -0.315      0.753      -0.124       0.090\n",
       "volatile_Log                   -0.0064      0.022     -0.290      0.772      -0.049       0.037\n",
       "productionCommodity_Log         0.0173      0.032      0.536      0.592      -0.046       0.081\n",
       "regulatory_Log                  0.0116      0.150      0.077      0.939      -0.283       0.306\n",
       "supplyDemandBalance_Log        -0.0076      0.005     -1.397      0.163      -0.018       0.003\n",
       "exploration_Log                -0.3286      0.193     -1.703      0.088      -0.707       0.049\n",
       "accidents_Log                   0.1832      0.146      1.253      0.210      -0.103       0.470\n",
       "supplyDemandBalanceForecast    -0.0075      0.025     -0.302      0.763      -0.056       0.041\n",
       "consumptionCommodity           -0.0350      0.050     -0.696      0.486      -0.133       0.063\n",
       "priceTrend                      0.0174      0.017      1.018      0.309      -0.016       0.051\n",
       "buyVsSellForecast              -0.0840      0.099     -0.852      0.394      -0.277       0.109\n",
       "feelingsVsTruth                -0.0013      0.004     -0.337      0.736      -0.009       0.006\n",
       "timeliness                     -0.0364      0.018     -2.049      0.040      -0.071      -0.002\n",
       "confidence                      0.0491      0.052      0.946      0.344      -0.053       0.151\n",
       "popularity                  -1.883e-07   1.74e-07     -1.081      0.279    -5.3e-07    1.53e-07\n",
       "general_sentiment               0.0097      0.005      2.011      0.044       0.000       0.019\n",
       "positive_outlook               -0.0164      0.012     -1.363      0.173      -0.040       0.007\n",
       "price_lag_7                     0.0006      0.000      3.107      0.002       0.000       0.001\n",
       "month-1                        -0.0025      0.019     -0.131      0.895      -0.039       0.034\n",
       "month-2                         0.0009      0.018      0.047      0.962      -0.035       0.037\n",
       "month-3                         0.0052      0.018      0.287      0.774      -0.030       0.041\n",
       "month-4                         0.0061      0.018      0.340      0.734      -0.029       0.041\n",
       "month-5                         0.0040      0.012      0.332      0.740      -0.019       0.027\n",
       "month-6                        -0.0061      0.011     -0.557      0.577      -0.028       0.015\n",
       "month-7                        -0.0089      0.011     -0.821      0.412      -0.030       0.012\n",
       "month-8                        -0.0016      0.011     -0.155      0.877      -0.023       0.019\n",
       "month-9                         0.0015      0.011      0.140      0.889      -0.020       0.023\n",
       "month-10                        0.0032      0.011      0.290      0.772      -0.019       0.025\n",
       "month-11                       -0.0003      0.012     -0.025      0.980      -0.024       0.024\n",
       "month-12                       -0.0014      0.012     -0.115      0.909      -0.026       0.023\n",
       "dayofweek-0                     0.0006      0.001      0.750      0.453      -0.001       0.002\n",
       "dayofweek-1                    -0.0017      0.001     -2.874      0.004      -0.003      -0.001\n",
       "dayofweek-2                    -0.0015      0.001     -2.545      0.011      -0.003      -0.000\n",
       "dayofweek-3                     0.0006      0.001      0.983      0.325      -0.001       0.002\n",
       "dayofweek-4                     0.0014      0.001      1.688      0.091      -0.000       0.003\n",
       "dayofweek-5                     0.0004      0.002      0.180      0.857      -0.004       0.005\n",
       "dayofweek-6                     0.0002      0.001      0.244      0.807      -0.002       0.002\n",
       "weekend                         0.0006      0.001      0.427      0.669      -0.002       0.003\n",
       "sunday                          0.0002      0.001      0.244      0.807      -0.002       0.002\n",
       "ar.L1                          -0.2509      1.663     -0.151      0.880      -3.511       3.009\n",
       "ma.L1                           0.2428      1.666      0.146      0.884      -3.022       3.508\n",
       "sigma2                          0.0002   3.68e-06     62.049      0.000       0.000       0.000\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):              4976.23\n",
       "Prob(Q):                              0.98   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               2.25   Skew:                            -0.39\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         9.68\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 9.93e+23. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm #time-series analysis for python\n",
    "\n",
    "arimaxAutoLog = sm.tsa.statespace.SARIMAX(traLog,order=(1,1,1),seasonal_order=(0,0,0,0),exog = exog_train, freq='D',\n",
    "                                  enforce_stationarity=False, enforce_invertibility=False,).fit()\n",
    "arimaxAutoLog.summary()\n",
    "#We can use SARIMAX model as ARIMAX when seasonal_order is (0,0,0,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the performance between the same algorithm with slightly different hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ARIMAX Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMAX baseline model MSE for training: 3.545493106631011\n",
      "ARIMAX model MSE:5.658751526542011\n"
     ]
    }
   ],
   "source": [
    "res = arimax.resid\n",
    "sumOfSquares = 0\n",
    "for r in res:\n",
    "    sumOfSquares += r**2\n",
    "\n",
    "mse_train = sumOfSquares / len(res)\n",
    "print('ARIMAX baseline model MSE for training: {}'.format(mse_train))\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "st = str(exog_test.reset_index().iloc[0, 0])[:10]\n",
    "en = str(exog_test.reset_index().iloc[-1, 0])[:10]\n",
    "\n",
    "pred_train = arimax.fittedvalues\n",
    "pred_train.index = pred_train.index.to_timestamp()\n",
    "pred = arimax.predict(st, en, exog = exog_test)\n",
    "print('ARIMAX model MSE:{}'.format(mean_squared_error(tes,pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b82ca04fa0>"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(13, 7))\n",
    "ax.plot(tra, label='Real', linewidth=2)\n",
    "ax.plot(pred_train, label='Base Model', linewidth=2)\n",
    "ax.legend(loc='best', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "tes.index = tes.index.to_timestamp(freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b83119b5e0>"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(13, 7))\n",
    "ax.plot(tes, label='Real Test', linewidth=2)\n",
    "ax.plot(pred, label='Predicted Test by Base Model', linewidth=2)\n",
    "ax.legend(loc='best', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMAX model MSE:3.5454931066310094\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "st = str(exog_train.reset_index().iloc[0, 0])[:10]\n",
    "en = str(exog_train.reset_index().iloc[-1, 0])[:10]\n",
    "\n",
    "pred_train = arimax.fittedvalues\n",
    "print('ARIMAX model MSE:{}'.format(mean_squared_error(tra,pred_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auto ARIMAX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMAX baseline model MSE for training: 4.5450856129313735\n",
      "ARIMAX model MSE:149.5412058139596\n"
     ]
    }
   ],
   "source": [
    "res = arimaxAuto.resid\n",
    "sumOfSquares = 0\n",
    "for r in res:\n",
    "    sumOfSquares += r**2\n",
    "\n",
    "mse_train = sumOfSquares / len(res)\n",
    "print('ARIMAX baseline model MSE for training: {}'.format(mse_train))\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "st = str(exog_test.reset_index().iloc[0, 0])[:10]\n",
    "en = str(exog_test.reset_index().iloc[-1, 0])[:10]\n",
    "\n",
    "pred_train = arimaxAuto.fittedvalues\n",
    "pred = arimaxAuto.predict(st, en, exog = exog_test)\n",
    "print('ARIMAX model MSE:{}'.format(mean_squared_error(tes,pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b83180cb80>"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(13, 7))\n",
    "ax.plot(tra, label='Real', linewidth=2)\n",
    "ax.plot(pred_train, label='Auto ARIMAX', linewidth=2)\n",
    "ax.legend(loc='best', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auto ARIMAX for Price Log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMAX baseline model MSE for training: 0.007961174274846725\n",
      "ARIMAX model MSE:157.64244800368525\n"
     ]
    }
   ],
   "source": [
    "res = arimaxAutoLog.resid\n",
    "sumOfSquares = 0\n",
    "for r in res:\n",
    "    sumOfSquares += r**2\n",
    "\n",
    "mse_train = sumOfSquares / len(res)\n",
    "print('ARIMAX baseline model MSE for training: {}'.format(mse_train))\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "st = str(exog_test.reset_index().iloc[0, 0])[:10]\n",
    "en = str(exog_test.reset_index().iloc[-1, 0])[:10]\n",
    "\n",
    "pred_train = arimaxAutoLog.fittedvalues\n",
    "pred = arimaxAutoLog.predict(st, en, exog = exog_test)\n",
    "pred = np.exp(pred)\n",
    "print('ARIMAX model MSE:{}'.format(mean_squared_error(tes,pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b82e0e6d30>"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(13, 7))\n",
    "ax.plot(tes, label='Real', linewidth=2)\n",
    "ax.plot(pred, label='ARIMAX Log Price', linewidth=2)\n",
    "ax.legend(loc='best', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot Test vs Pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "tesPlt = tes.copy()\n",
    "predPlt = pred.copy()\n",
    "predAutoPlt = predAuto.copy()\n",
    "predAuto2Plt = predAuto2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b82e18cbb0>"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(13, 7))\n",
    "ax.plot(tesPlt, label='Real', linewidth=12)\n",
    "ax.plot(predPlt, label='ARIMAX Baseline', linewidth=2)\n",
    "ax.plot(predAutoPlt, label='Auto ARIMAX', linewidth=5)\n",
    "ax.plot(predAuto2Plt, label='Auto ARIMAX Price Log', linewidth=2)\n",
    "ax.legend(loc='best', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output the model to a pickle file for comparison with other models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "path = \"../Models/\"\n",
    "filename = \"arimax.sav\" #change according to your model name\n",
    "filepath = path + filename\n",
    "pickle.dump(arimax, open(filepath, 'wb'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "7767d01aa5fde26fe11d0d80e3ff9b3b56ae7326b4cbae5d8d51b527e2e0868f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}