ARIMA_Covid_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import statsmodels.api as sm\n",
    "import itertools"
    
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases= pd.read_csv(\"C:/Users/Checkout/Downloads/coviddata/covid_19_clean_complete.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49063</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>0.186400</td>\n",
       "      <td>6.613100</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>865</td>\n",
       "      <td>14</td>\n",
       "      <td>734</td>\n",
       "      <td>117</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49064</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>1691</td>\n",
       "      <td>483</td>\n",
       "      <td>833</td>\n",
       "      <td>375</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49065</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Comoros</td>\n",
       "      <td>-11.645500</td>\n",
       "      <td>43.333300</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>354</td>\n",
       "      <td>7</td>\n",
       "      <td>328</td>\n",
       "      <td>19</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49066</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>38.861000</td>\n",
       "      <td>71.276100</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>7235</td>\n",
       "      <td>60</td>\n",
       "      <td>6028</td>\n",
       "      <td>1147</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49067</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Lesotho</td>\n",
       "      <td>-29.610000</td>\n",
       "      <td>28.233600</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>505</td>\n",
       "      <td>12</td>\n",
       "      <td>128</td>\n",
       "      <td>365</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49068 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Province/State         Country/Region        Lat       Long        Date  \\\n",
       "0                NaN            Afghanistan  33.939110  67.709953  2020-01-22   \n",
       "1                NaN                Albania  41.153300  20.168300  2020-01-22   \n",
       "2                NaN                Algeria  28.033900   1.659600  2020-01-22   \n",
       "3                NaN                Andorra  42.506300   1.521800  2020-01-22   \n",
       "4                NaN                 Angola -11.202700  17.873900  2020-01-22   \n",
       "...              ...                    ...        ...        ...         ...   \n",
       "49063            NaN  Sao Tome and Principe   0.186400   6.613100  2020-07-27   \n",
       "49064            NaN                  Yemen  15.552727  48.516388  2020-07-27   \n",
       "49065            NaN                Comoros -11.645500  43.333300  2020-07-27   \n",
       "49066            NaN             Tajikistan  38.861000  71.276100  2020-07-27   \n",
       "49067            NaN                Lesotho -29.610000  28.233600  2020-07-27   \n",
       "\n",
       "       Confirmed  Deaths  Recovered  Active             WHO Region  \n",
       "0              0       0          0       0  Eastern Mediterranean  \n",
       "1              0       0          0       0                 Europe  \n",
       "2              0       0          0       0                 Africa  \n",
       "3              0       0          0       0                 Europe  \n",
       "4              0       0          0       0                 Africa  \n",
       "...          ...     ...        ...     ...                    ...  \n",
       "49063        865      14        734     117                 Africa  \n",
       "49064       1691     483        833     375  Eastern Mediterranean  \n",
       "49065        354       7        328      19                 Africa  \n",
       "49066       7235      60       6028    1147                 Europe  \n",
       "49067        505      12        128     365                 Africa  \n",
       "\n",
       "[49068 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "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": [
    "cases['Active'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Province/State    34404\n",
       "Country/Region        0\n",
       "Lat                   0\n",
       "Long                  0\n",
       "Date                  0\n",
       "Confirmed             0\n",
       "Deaths                0\n",
       "Recovered             0\n",
       "Active                0\n",
       "WHO Region            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49068, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases['Date']=pd.to_datetime(cases['Date'])\n",
    "vaccin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "active = cases[['Date','Country/Region','Active']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49063</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49064</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49065</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Comoros</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49066</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>1147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49067</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Lesotho</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49068 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date         Country/Region  Active\n",
       "0     2020-01-22            Afghanistan       0\n",
       "1     2020-01-22                Albania       0\n",
       "2     2020-01-22                Algeria       0\n",
       "3     2020-01-22                Andorra       0\n",
       "4     2020-01-22                 Angola       0\n",
       "...          ...                    ...     ...\n",
       "49063 2020-07-27  Sao Tome and Principe     117\n",
       "49064 2020-07-27                  Yemen     375\n",
       "49065 2020-07-27                Comoros      19\n",
       "49066 2020-07-27             Tajikistan    1147\n",
       "49067 2020-07-27                Lesotho     365\n",
       "\n",
       "[49068 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_active=cases.groupby([\"Country/Region\",\"Date\"])['Active'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Country/Region</th>\n",
       "      <th>Date</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country/Region       Date  Active\n",
       "0    Afghanistan 2020-01-22       0\n",
       "1    Afghanistan 2020-01-23       0\n",
       "2    Afghanistan 2020-01-24       0\n",
       "3    Afghanistan 2020-01-25       0\n",
       "4    Afghanistan 2020-01-26       0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_active.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rtoll = total_active.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Country/Region</th>\n",
       "      <th>Date</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country/Region       Date  Active\n",
       "0    Afghanistan 2020-01-22       0\n",
       "1    Afghanistan 2020-01-23       0\n",
       "2    Afghanistan 2020-01-24       0\n",
       "3    Afghanistan 2020-01-25       0\n",
       "4    Afghanistan 2020-01-26       0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rtoll.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "rtoll.set_index('Date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "rtoll.index = pd.to_datetime(rtoll.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Country/Region</th>\n",
       "      <th>Active</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>2020-01-22</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country/Region  Active\n",
       "Date                             \n",
       "2020-01-22    Afghanistan       0\n",
       "2020-01-23    Afghanistan       0\n",
       "2020-01-24    Afghanistan       0\n",
       "2020-01-25    Afghanistan       0\n",
       "2020-01-26    Afghanistan       0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rtoll.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] =5,5\n",
    "rtoll.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results = adfuller(rtoll['Active'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adfuller_test (deaths):\n",
    "    result = adfuller(deaths)\n",
    "    labels=['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
    "    for value,label in zip(result,labels):\n",
    "        print(label+':'+ str(value))\n",
    "    if(result[1]<=0.05):\n",
    "        print(\"Stationary\")\n",
    "    else:\n",
    "        print(\"Not Stationary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Test Statistic:-16.651928264461535\n",
      "p-value:1.6012826481123947e-29\n",
      "#Lags Used:1\n",
      "Number of Observations Used:35154\n",
      "Stationary\n"
     ]
    }
   ],
   "source": [
    "adfuller_test(rtoll['Active'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Region    0\n",
       "Active            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rtoll.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1=fig.add_subplot(211)\n",
    "fig=sm.graphics.tsa.plot_acf(rtoll['Active'].iloc[1:],lags=40,ax=ax1)\n",
    "ax2=fig.add_subplot(212)\n",
    "fig=sm.graphics.tsa.plot_pacf(rtoll['Active'].iloc[1:],lags=40,ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Country/Region  Active\n",
      "Date                             \n",
      "2020-01-22        Albania       0\n",
      "2020-01-23        Albania       0\n",
      "2020-01-24        Albania       0\n",
      "2020-01-25        Albania       0\n",
      "2020-01-26        Albania       0\n",
      "...                   ...     ...\n",
      "2020-07-23        Albania    1820\n",
      "2020-07-24        Albania    1834\n",
      "2020-07-25        Albania    1866\n",
      "2020-07-26        Albania    1943\n",
      "2020-07-27        Albania    1991\n",
      "\n",
      "[188 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "selected_country=\"Albania\"\n",
    "country_df = rtoll[rtoll['Country/Region']==selected_country]\n",
    "print(country_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "country_df.index = pd.to_datetime(country_df.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = q = d = range(0,2)\n",
    "pdq = list(itertools.product(p,d,q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 0)- AIC:4942.7248424817135\n",
      "ARIMA(0, 0, 1)- AIC:4695.592727276466\n",
      "ARIMA(0, 1, 0)- AIC:3627.2752252888026\n",
      "ARIMA(0, 1, 1)- AIC:3519.9307222236307\n",
      "ARIMA(1, 0, 0)- AIC:3728.2362567461487\n",
      "ARIMA(1, 1, 0)- AIC:3378.586040052179\n"
     ]
    }
   ],
   "source": [
    "for param in pdq:\n",
    "        try:\n",
    "            model=ARIMA(country_df['Active'].dropna(),order=param)\n",
    "            results=model.fit()\n",
    "            print('ARIMA{}- AIC:{}'.format(param,results.aic))\n",
    "        except:\n",
    "            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ARIMA(country_df['Active'].dropna(),order=(0,1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "results=model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>const</th>          <td> 2644.1548</td> <td>  343.433</td> <td>    7.699</td> <td> 0.000</td> <td> 1971.039</td> <td> 3317.270</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.Active</th> <td>    0.6181</td> <td>    0.050</td> <td>   12.366</td> <td> 0.000</td> <td>    0.520</td> <td>    0.716</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.tsa.arima_model.ARIMAResultsWrapper at 0x2c281734c40>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_df['Forecast']=results.predict(start=pd.to_datetime('2020-01-24'),dynamic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2020-07-23    2644.154827\n",
       "2020-07-24    2644.154827\n",
       "2020-07-25    2644.154827\n",
       "2020-07-26    2644.154827\n",
       "2020-07-27    2644.154827\n",
       "Name: Forecast, dtype: float64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df['Forecast'].tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-07-27 00:00:00')"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df.index[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "289\n"
     ]
    }
   ],
   "source": [
    "from datetime import date\n",
    "f_date = pd.to_datetime('2020-07-27')\n",
    "end='2021-05-12'\n",
    "l_date=pd.to_datetime(end)\n",
    "delta = l_date - f_date\n",
    "print(delta.days)\n",
    "days=delta.days\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = results.forecast(steps=days)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        973886.6415296 ,  976530.79635611,  979174.95118263,\n",
       "        981819.10600914,  984463.26083565,  987107.41566217,\n",
       "        989751.57048868,  992395.72531519,  995039.8801417 ,\n",
       "        997684.03496822, 1000328.18979473, 1002972.34462124,\n",
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       "       1013548.96392729, 1016193.1187538 , 1018837.27358032,\n",
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       "       1029413.89288637, 1032058.04771288, 1034702.20253939,\n",
       "       1037346.35736591, 1039990.51219242, 1042634.66701893,\n",
       "       1045278.82184544, 1047922.97667196, 1050567.13149847,\n",
       "       1053211.28632498, 1055855.44115149, 1058499.59597801,\n",
       "       1061143.75080452, 1063787.90563103, 1066432.06045754,\n",
       "       1069076.21528406, 1071720.37011057, 1074364.52493708,\n",
       "       1077008.6797636 , 1079652.83459011, 1082296.98941662,\n",
       "       1084941.14424313, 1087585.29906965, 1090229.45389616,\n",
       "       1092873.60872267, 1095517.76354918, 1098161.9183757 ,\n",
       "       1100806.07320221, 1103450.22802872, 1106094.38285523,\n",
       "       1108738.53768175, 1111382.69250826, 1114026.84733477,\n",
       "       1116671.00216128, 1119315.1569878 , 1121959.31181431,\n",
       "       1124603.46664082, 1127247.62146733, 1129891.77629385,\n",
       "       1132535.93112036, 1135180.08594687, 1137824.24077338,\n",
       "       1140468.3955999 , 1143112.55042641, 1145756.70525292,\n",
       "       1148400.86007943, 1151045.01490595, 1153689.16973246,\n",
       "       1156333.32455897, 1158977.47938548, 1161621.634212  ,\n",
       "       1164265.78903851, 1166909.94386502, 1169554.09869153,\n",
       "       1172198.25351805, 1174842.40834456, 1177486.56317107,\n",
       "       1180130.71799758, 1182774.8728241 , 1185419.02765061,\n",
       "       1188063.18247712, 1190707.33730364, 1193351.49213015,\n",
       "       1195995.64695666, 1198639.80178317, 1201283.95660969,\n",
       "       1203928.1114362 , 1206572.26626271, 1209216.42108922,\n",
       "       1211860.57591574, 1214504.73074225, 1217148.88556876,\n",
       "       1219793.04039527, 1222437.19522179, 1225081.3500483 ,\n",
       "       1227725.50487481, 1230369.65970132, 1233013.81452784,\n",
       "       1235657.96935435, 1238302.12418086, 1240946.27900737,\n",
       "       1243590.43383389, 1246234.5886604 , 1248878.74348691,\n",
       "       1251522.89831342, 1254167.05313994, 1256811.20796645,\n",
       "       1259455.36279296])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.offsets import DateOffset\n",
    "future_dates = [country_df.index[-1]+ DateOffset(days=x)for x in range(0,days)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_dates_df = pd.DataFrame(index=future_dates[1:],columns=country_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_df = pd.concat([country_df,future_dates_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>Forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-05-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country/Region Active  Forecast\n",
       "2021-05-06            NaN    NaN       NaN\n",
       "2021-05-07            NaN    NaN       NaN\n",
       "2021-05-08            NaN    NaN       NaN\n",
       "2021-05-09            NaN    NaN       NaN\n",
       "2021-05-10            NaN    NaN       NaN\n",
       "2021-05-11            NaN    NaN       NaN"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_df.tail(6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>Forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>China</td>\n",
       "      <td>503</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>China</td>\n",
       "      <td>595</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>China</td>\n",
       "      <td>858</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>China</td>\n",
       "      <td>1325</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>China</td>\n",
       "      <td>1970</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.732618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.732618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.732618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.732618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.732618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>476 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country/Region Active   Forecast\n",
       "2020-01-22          China    503        NaN\n",
       "2020-01-23          China    595        NaN\n",
       "2020-01-24          China    858        NaN\n",
       "2020-01-25          China   1325        NaN\n",
       "2020-01-26          China   1970        NaN\n",
       "...                   ...    ...        ...\n",
       "2021-05-07            NaN    NaN  14.732618\n",
       "2021-05-08            NaN    NaN  14.732618\n",
       "2021-05-09            NaN    NaN  14.732618\n",
       "2021-05-10            NaN    NaN  14.732618\n",
       "2021-05-11            NaN    NaN  14.732618\n",
       "\n",
       "[476 rows x 3 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_df['Forecast'] = results.predict(start=f_date,end=l_date,dynamic=False)\n",
    "future_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# future_df[['Active','Forecast']].plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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