COVID-19 Case Forecaster using ARIMA Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COVID-19 Case Forecaster using ARIMA Models\n",
    "\n",
    "Using standard ARIMA forecasting techniques, I looked to predict the future of corona-virus cases in India"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standard Library imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "sns.set(style='whitegrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pmdarima helps efficiently calculate the best p,d,q values for ARIMA models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pmdarima import auto_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imported the dataset called confirmed.csv. I need to remove all zeros from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cvd19_confirmed = pd.read_csv('confirmed.csv', index_col='Date', parse_dates=True)[50:]\n",
    "cvd19_confirmed = pd.read_csv('confirmed.csv', index_col='Date', parse_dates=True)[8:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvd19_confirmed.index.freq = 'D'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-02</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-03</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            Unnamed: 0  India\n",
       "Date                         \n",
       "2020-01-30           8      1\n",
       "2020-01-31           9      1\n",
       "2020-02-01          10      1\n",
       "2020-02-02          11      2\n",
       "2020-02-03          12      3"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvd19_confirmed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-22</th>\n",
       "      <td>182</td>\n",
       "      <td>1238798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23</th>\n",
       "      <td>183</td>\n",
       "      <td>1288108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-24</th>\n",
       "      <td>184</td>\n",
       "      <td>1337024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-25</th>\n",
       "      <td>185</td>\n",
       "      <td>1385635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-26</th>\n",
       "      <td>186</td>\n",
       "      <td>1435616</td>\n",
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      "text/plain": [
       "            Unnamed: 0    India\n",
       "Date                           \n",
       "2020-07-22         182  1238798\n",
       "2020-07-23         183  1288108\n",
       "2020-07-24         184  1337024\n",
       "2020-07-25         185  1385635\n",
       "2020-07-26         186  1435616"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvd19_confirmed.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 179 entries, 2020-01-30 to 2020-07-26\n",
      "Freq: D\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype\n",
      "---  ------      --------------  -----\n",
      " 0   Unnamed: 0  179 non-null    int64\n",
      " 1   India       179 non-null    int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 4.2 KB\n"
     ]
    }
   ],
   "source": [
    "cvd19_confirmed.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting index, cleaning blank columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvd19_confirmed = cvd19_confirmed.drop('Unnamed: 0', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cvd19_confirmed['India'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>India</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>2020-01-30</th>\n",
       "      <td>1</td>\n",
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       "      <th>2020-01-31</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2020-02-02</th>\n",
       "      <td>2</td>\n",
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       "      <th>2020-02-03</th>\n",
       "      <td>3</td>\n",
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       "      <th>2020-02-04</th>\n",
       "      <td>3</td>\n",
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       "      <th>2020-02-05</th>\n",
       "      <td>3</td>\n",
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       "      <th>2020-02-06</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-08</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-09</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-14</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-15</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-16</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-17</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-18</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "            India\n",
       "Date             \n",
       "2020-01-30      1\n",
       "2020-01-31      1\n",
       "2020-02-01      1\n",
       "2020-02-02      2\n",
       "2020-02-03      3\n",
       "2020-02-04      3\n",
       "2020-02-05      3\n",
       "2020-02-06      3\n",
       "2020-02-07      3\n",
       "2020-02-08      3\n",
       "2020-02-09      3\n",
       "2020-02-10      3\n",
       "2020-02-11      3\n",
       "2020-02-12      3\n",
       "2020-02-13      3\n",
       "2020-02-14      3\n",
       "2020-02-15      3\n",
       "2020-02-16      3\n",
       "2020-02-17      3\n",
       "2020-02-18      3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvd19_confirmed.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing plot_acf, plot_pacf for understanding nature of data. \n",
    "### Importing seasonal_decompose to find seasonality and trend of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "decompose = seasonal_decompose(cvd19_confirmed['India'], model='multiplicative');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data is non seasonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decompose.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acf=plot_acf(cvd19_confirmed['India'], lags=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pacf=plot_pacf(cvd19_confirmed['India'], lags=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Strong AR System. p = 1, q = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      "Fit ARIMA(2,2,2)x(0,0,0,0) [intercept=True]; AIC=2929.873, BIC=2948.929, Time=0.880 seconds\n",
      "Fit ARIMA(0,2,0)x(0,0,0,0) [intercept=True]; AIC=2953.869, BIC=2960.221, Time=0.021 seconds\n",
      "Fit ARIMA(1,2,0)x(0,0,0,0) [intercept=True]; AIC=2948.609, BIC=2958.137, Time=0.048 seconds\n",
      "Fit ARIMA(0,2,1)x(0,0,0,0) [intercept=True]; AIC=2939.717, BIC=2949.245, Time=0.235 seconds\n",
      "Fit ARIMA(0,2,0)x(0,0,0,0) [intercept=False]; AIC=2965.293, BIC=2968.469, Time=0.018 seconds\n",
      "Fit ARIMA(1,2,2)x(0,0,0,0) [intercept=True]; AIC=2937.313, BIC=2953.194, Time=0.181 seconds\n",
      "Fit ARIMA(2,2,1)x(0,0,0,0) [intercept=True]; AIC=2945.955, BIC=2961.836, Time=0.117 seconds\n",
      "Fit ARIMA(3,2,2)x(0,0,0,0) [intercept=True]; AIC=2883.001, BIC=2905.234, Time=0.976 seconds\n",
      "Fit ARIMA(3,2,1)x(0,0,0,0) [intercept=True]; AIC=2930.968, BIC=2950.025, Time=0.201 seconds\n",
      "Fit ARIMA(4,2,2)x(0,0,0,0) [intercept=True]; AIC=2904.072, BIC=2929.481, Time=1.118 seconds\n",
      "Near non-invertible roots for order (4, 2, 2)(0, 0, 0, 0); setting score to inf (at least one inverse root too close to the border of the unit circle: 1.000)\n",
      "Fit ARIMA(3,2,3)x(0,0,0,0) [intercept=True]; AIC=2922.017, BIC=2947.427, Time=1.051 seconds\n",
      "Fit ARIMA(2,2,3)x(0,0,0,0) [intercept=True]; AIC=2906.345, BIC=2928.578, Time=1.018 seconds\n",
      "Near non-invertible roots for order (2, 2, 3)(0, 0, 0, 0); setting score to inf (at least one inverse root too close to the border of the unit circle: 0.998)\n",
      "Fit ARIMA(4,2,1)x(0,0,0,0) [intercept=True]; AIC=2931.294, BIC=2953.527, Time=0.314 seconds\n",
      "Fit ARIMA(4,2,3)x(0,0,0,0) [intercept=True]; AIC=2876.103, BIC=2904.689, Time=1.185 seconds\n",
      "Near non-invertible roots for order (4, 2, 3)(0, 0, 0, 0); setting score to inf (at least one inverse root too close to the border of the unit circle: 1.000)\n",
      "Total fit time: 7.389 seconds\n"
     ]
    }
   ],
   "source": [
    "pmd = auto_arima(cvd19_confirmed['India'], seasonal=False, trace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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>179</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(4, 2, 3)</td> <th>  Log Likelihood     </th> <td>-1429.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 30 Jul 2020</td> <th>  AIC                </th> <td>2876.103</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>12:49:30</td>     <th>  BIC                </th> <td>2904.689</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>2887.696</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 179</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>    6.7633</td> <td>   20.497</td> <td>    0.330</td> <td> 0.741</td> <td>  -33.409</td> <td>   46.936</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    0.6823</td> <td>    0.066</td> <td>   10.276</td> <td> 0.000</td> <td>    0.552</td> <td>    0.812</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>     <td>    0.3401</td> <td>    0.066</td> <td>    5.117</td> <td> 0.000</td> <td>    0.210</td> <td>    0.470</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>     <td>   -0.4518</td> <td>    0.072</td> <td>   -6.243</td> <td> 0.000</td> <td>   -0.594</td> <td>   -0.310</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4</th>     <td>    0.4266</td> <td>    0.055</td> <td>    7.739</td> <td> 0.000</td> <td>    0.319</td> <td>    0.535</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>     <td>   -0.7064</td> <td>    0.092</td> <td>   -7.685</td> <td> 0.000</td> <td>   -0.887</td> <td>   -0.526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>     <td>   -0.8897</td> <td>    0.108</td> <td>   -8.208</td> <td> 0.000</td> <td>   -1.102</td> <td>   -0.677</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3</th>     <td>    0.8167</td> <td>    0.071</td> <td>   11.491</td> <td> 0.000</td> <td>    0.677</td> <td>    0.956</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td> 5.213e+05</td> <td>  1.9e-06</td> <td> 2.74e+11</td> <td> 0.000</td> <td> 5.21e+05</td> <td> 5.21e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>           <td>87.26</td> <th>  Jarque-Bera (JB):  </th> <td>365.47</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                 <td>0.00</td>  <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>247.89</td> <th>  Skew:              </th>  <td>0.75</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>     <td>0.00</td>  <th>  Kurtosis:          </th>  <td>9.88</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 4.47e+30. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  179\n",
       "Model:               SARIMAX(4, 2, 3)   Log Likelihood               -1429.052\n",
       "Date:                Thu, 30 Jul 2020   AIC                           2876.103\n",
       "Time:                        12:49:30   BIC                           2904.689\n",
       "Sample:                             0   HQIC                          2887.696\n",
       "                                - 179                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept      6.7633     20.497      0.330      0.741     -33.409      46.936\n",
       "ar.L1          0.6823      0.066     10.276      0.000       0.552       0.812\n",
       "ar.L2          0.3401      0.066      5.117      0.000       0.210       0.470\n",
       "ar.L3         -0.4518      0.072     -6.243      0.000      -0.594      -0.310\n",
       "ar.L4          0.4266      0.055      7.739      0.000       0.319       0.535\n",
       "ma.L1         -0.7064      0.092     -7.685      0.000      -0.887      -0.526\n",
       "ma.L2         -0.8897      0.108     -8.208      0.000      -1.102      -0.677\n",
       "ma.L3          0.8167      0.071     11.491      0.000       0.677       0.956\n",
       "sigma2      5.213e+05    1.9e-06   2.74e+11      0.000    5.21e+05    5.21e+05\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       87.26   Jarque-Bera (JB):               365.47\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):             247.89   Skew:                             0.75\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         9.88\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 4.47e+30. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pmd.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing ARIMA library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "para=pmd.get_params()['order']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=ARIMA(cvd19_confirmed['India'], order=para)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_model=model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16dfee549a0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit_model.resid.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_pred = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_pred['Predictions'] = fit_model.predict(start=2, end=len(cvd19_confirmed)+30, typ='levels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-08-15</th>\n",
       "      <td>2.619785e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-16</th>\n",
       "      <td>2.690374e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-17</th>\n",
       "      <td>2.761992e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-18</th>\n",
       "      <td>2.832843e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-19</th>\n",
       "      <td>2.905369e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-20</th>\n",
       "      <td>2.980419e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-21</th>\n",
       "      <td>3.055224e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-22</th>\n",
       "      <td>3.130004e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-23</th>\n",
       "      <td>3.207445e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <td>3.286286e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>3.364330e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <td>3.443687e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Predictions\n",
       "2020-08-15  2.619785e+06\n",
       "2020-08-16  2.690374e+06\n",
       "2020-08-17  2.761992e+06\n",
       "2020-08-18  2.832843e+06\n",
       "2020-08-19  2.905369e+06\n",
       "2020-08-20  2.980419e+06\n",
       "2020-08-21  3.055224e+06\n",
       "2020-08-22  3.130004e+06\n",
       "2020-08-23  3.207445e+06\n",
       "2020-08-24  3.286286e+06\n",
       "2020-08-25  3.364330e+06\n",
       "2020-08-26  3.443687e+06"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_pred.tail(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16dff299070>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvd19_confirmed.plot(figsize=(20, 8), legend=True, colormap='Set1', alpha=0.5, title='COVID-19')\n",
    "future_pred['Predictions'].plot(legend=True, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_pred['New cases'] = future_pred['Predictions'].diff(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predictions</th>\n",
       "      <th>New cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>283.612732</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-02</th>\n",
       "      <td>333.744409</td>\n",
       "      <td>50.131677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-03</th>\n",
       "      <td>655.062712</td>\n",
       "      <td>321.318303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-04</th>\n",
       "      <td>10.341610</td>\n",
       "      <td>-644.721101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05</th>\n",
       "      <td>7.644752</td>\n",
       "      <td>-2.696858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Predictions   New cases\n",
       "2020-02-01   283.612732         NaN\n",
       "2020-02-02   333.744409   50.131677\n",
       "2020-02-03   655.062712  321.318303\n",
       "2020-02-04    10.341610 -644.721101\n",
       "2020-02-05     7.644752   -2.696858"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predictions</th>\n",
       "      <th>New cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-28</th>\n",
       "      <td>1.534936e+06</td>\n",
       "      <td>49530.576831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-29</th>\n",
       "      <td>1.586995e+06</td>\n",
       "      <td>52058.339133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-30</th>\n",
       "      <td>1.640722e+06</td>\n",
       "      <td>53727.230742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>1.693682e+06</td>\n",
       "      <td>52960.760333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-01</th>\n",
       "      <td>1.747680e+06</td>\n",
       "      <td>53997.705015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-02</th>\n",
       "      <td>1.804453e+06</td>\n",
       "      <td>56772.324601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-03</th>\n",
       "      <td>1.861524e+06</td>\n",
       "      <td>57071.043515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-04</th>\n",
       "      <td>1.918113e+06</td>\n",
       "      <td>56589.903079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-05</th>\n",
       "      <td>1.977000e+06</td>\n",
       "      <td>58886.199584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-06</th>\n",
       "      <td>2.037887e+06</td>\n",
       "      <td>60887.499927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-07</th>\n",
       "      <td>2.098113e+06</td>\n",
       "      <td>60225.337081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-08</th>\n",
       "      <td>2.159001e+06</td>\n",
       "      <td>60888.536768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-09</th>\n",
       "      <td>2.222703e+06</td>\n",
       "      <td>63702.171757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-10</th>\n",
       "      <td>2.287061e+06</td>\n",
       "      <td>64358.208487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-11</th>\n",
       "      <td>2.350760e+06</td>\n",
       "      <td>63698.465045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-12</th>\n",
       "      <td>2.416465e+06</td>\n",
       "      <td>65705.219218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-13</th>\n",
       "      <td>2.484461e+06</td>\n",
       "      <td>67995.818824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-14</th>\n",
       "      <td>2.551971e+06</td>\n",
       "      <td>67509.925755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-15</th>\n",
       "      <td>2.619785e+06</td>\n",
       "      <td>67814.292449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-16</th>\n",
       "      <td>2.690374e+06</td>\n",
       "      <td>70589.167547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-17</th>\n",
       "      <td>2.761992e+06</td>\n",
       "      <td>71617.698722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-18</th>\n",
       "      <td>2.832843e+06</td>\n",
       "      <td>70851.255019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-19</th>\n",
       "      <td>2.905369e+06</td>\n",
       "      <td>72525.470350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-20</th>\n",
       "      <td>2.980419e+06</td>\n",
       "      <td>75050.104491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-21</th>\n",
       "      <td>3.055224e+06</td>\n",
       "      <td>74804.861655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-22</th>\n",
       "      <td>3.130004e+06</td>\n",
       "      <td>74780.750272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-23</th>\n",
       "      <td>3.207445e+06</td>\n",
       "      <td>77440.741518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <td>3.286286e+06</td>\n",
       "      <td>78840.867729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>3.364330e+06</td>\n",
       "      <td>78044.196123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <td>3.443687e+06</td>\n",
       "      <td>79357.184006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Predictions     New cases\n",
       "2020-07-28  1.534936e+06  49530.576831\n",
       "2020-07-29  1.586995e+06  52058.339133\n",
       "2020-07-30  1.640722e+06  53727.230742\n",
       "2020-07-31  1.693682e+06  52960.760333\n",
       "2020-08-01  1.747680e+06  53997.705015\n",
       "2020-08-02  1.804453e+06  56772.324601\n",
       "2020-08-03  1.861524e+06  57071.043515\n",
       "2020-08-04  1.918113e+06  56589.903079\n",
       "2020-08-05  1.977000e+06  58886.199584\n",
       "2020-08-06  2.037887e+06  60887.499927\n",
       "2020-08-07  2.098113e+06  60225.337081\n",
       "2020-08-08  2.159001e+06  60888.536768\n",
       "2020-08-09  2.222703e+06  63702.171757\n",
       "2020-08-10  2.287061e+06  64358.208487\n",
       "2020-08-11  2.350760e+06  63698.465045\n",
       "2020-08-12  2.416465e+06  65705.219218\n",
       "2020-08-13  2.484461e+06  67995.818824\n",
       "2020-08-14  2.551971e+06  67509.925755\n",
       "2020-08-15  2.619785e+06  67814.292449\n",
       "2020-08-16  2.690374e+06  70589.167547\n",
       "2020-08-17  2.761992e+06  71617.698722\n",
       "2020-08-18  2.832843e+06  70851.255019\n",
       "2020-08-19  2.905369e+06  72525.470350\n",
       "2020-08-20  2.980419e+06  75050.104491\n",
       "2020-08-21  3.055224e+06  74804.861655\n",
       "2020-08-22  3.130004e+06  74780.750272\n",
       "2020-08-23  3.207445e+06  77440.741518\n",
       "2020-08-24  3.286286e+06  78840.867729\n",
       "2020-08-25  3.364330e+06  78044.196123\n",
       "2020-08-26  3.443687e+06  79357.184006"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_pred.tail(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16dfef32f10>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "future_pred['New cases'].plot(figsize=(12, 8))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}