scaai.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import io\n",
    "import requests\n",
    "\n",
    "\n",
    "def get_data(url):\n",
    "    s=requests.get(url).content\n",
    "    return pd.read_csv(io.StringIO(s.decode('utf-8')))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "case_time_series = get_data('https://api.covid19india.org/csv/latest/case_time_series.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Daily Confirmed</th>\n",
       "      <th>Total Confirmed</th>\n",
       "      <th>Daily Recovered</th>\n",
       "      <th>Total Recovered</th>\n",
       "      <th>Daily Deceased</th>\n",
       "      <th>Total Deceased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>21 May</td>\n",
       "      <td>6023</td>\n",
       "      <td>118223</td>\n",
       "      <td>3131</td>\n",
       "      <td>48553</td>\n",
       "      <td>148</td>\n",
       "      <td>3583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>22 May</td>\n",
       "      <td>6536</td>\n",
       "      <td>124759</td>\n",
       "      <td>3280</td>\n",
       "      <td>51833</td>\n",
       "      <td>142</td>\n",
       "      <td>3725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>23 May</td>\n",
       "      <td>6665</td>\n",
       "      <td>131422</td>\n",
       "      <td>2576</td>\n",
       "      <td>54409</td>\n",
       "      <td>142</td>\n",
       "      <td>3867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>24 May</td>\n",
       "      <td>7111</td>\n",
       "      <td>138535</td>\n",
       "      <td>3285</td>\n",
       "      <td>57694</td>\n",
       "      <td>156</td>\n",
       "      <td>4023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>25 May</td>\n",
       "      <td>6414</td>\n",
       "      <td>144949</td>\n",
       "      <td>3012</td>\n",
       "      <td>60706</td>\n",
       "      <td>150</td>\n",
       "      <td>4173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Daily Confirmed  Total Confirmed  Daily Recovered  \\\n",
       "112  21 May              6023           118223             3131   \n",
       "113  22 May              6536           124759             3280   \n",
       "114  23 May              6665           131422             2576   \n",
       "115  24 May              7111           138535             3285   \n",
       "116  25 May              6414           144949             3012   \n",
       "\n",
       "     Total Recovered  Daily Deceased  Total Deceased  \n",
       "112            48553             148            3583  \n",
       "113            51833             142            3725  \n",
       "114            54409             142            3867  \n",
       "115            57694             156            4023  \n",
       "116            60706             150            4173  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case_time_series.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=case_time_series[['Date','Total Confirmed']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "datetime_object = datetime.strptime('30 January', '%d %B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "daterange=[]\n",
    "for i in df.Date:\n",
    "    i=i+'2020'\n",
    "    datetime_object = datetime.strptime(i, '%d %B %Y')\n",
    "    daterange.append(datetime_object) #.strftime(\"%Y-%m-%d\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2020, 1, 30, 0, 0),\n",
       " datetime.datetime(2020, 1, 31, 0, 0),\n",
       " datetime.datetime(2020, 2, 1, 0, 0),\n",
       " datetime.datetime(2020, 2, 2, 0, 0),\n",
       " datetime.datetime(2020, 2, 3, 0, 0),\n",
       " datetime.datetime(2020, 2, 4, 0, 0),\n",
       " datetime.datetime(2020, 2, 5, 0, 0),\n",
       " datetime.datetime(2020, 2, 6, 0, 0),\n",
       " datetime.datetime(2020, 2, 7, 0, 0),\n",
       " datetime.datetime(2020, 2, 8, 0, 0),\n",
       " datetime.datetime(2020, 2, 9, 0, 0),\n",
       " datetime.datetime(2020, 2, 10, 0, 0),\n",
       " datetime.datetime(2020, 2, 11, 0, 0),\n",
       " datetime.datetime(2020, 2, 12, 0, 0),\n",
       " datetime.datetime(2020, 2, 13, 0, 0),\n",
       " datetime.datetime(2020, 2, 14, 0, 0),\n",
       " datetime.datetime(2020, 2, 15, 0, 0),\n",
       " datetime.datetime(2020, 2, 16, 0, 0),\n",
       " datetime.datetime(2020, 2, 17, 0, 0),\n",
       " datetime.datetime(2020, 2, 18, 0, 0),\n",
       " datetime.datetime(2020, 2, 19, 0, 0),\n",
       " datetime.datetime(2020, 2, 20, 0, 0),\n",
       " datetime.datetime(2020, 2, 21, 0, 0),\n",
       " datetime.datetime(2020, 2, 22, 0, 0),\n",
       " datetime.datetime(2020, 2, 23, 0, 0),\n",
       " datetime.datetime(2020, 2, 24, 0, 0),\n",
       " datetime.datetime(2020, 2, 25, 0, 0),\n",
       " datetime.datetime(2020, 2, 26, 0, 0),\n",
       " datetime.datetime(2020, 2, 27, 0, 0),\n",
       " datetime.datetime(2020, 2, 28, 0, 0),\n",
       " datetime.datetime(2020, 2, 29, 0, 0),\n",
       " datetime.datetime(2020, 3, 1, 0, 0),\n",
       " datetime.datetime(2020, 3, 2, 0, 0),\n",
       " datetime.datetime(2020, 3, 3, 0, 0),\n",
       " datetime.datetime(2020, 3, 4, 0, 0),\n",
       " datetime.datetime(2020, 3, 5, 0, 0),\n",
       " datetime.datetime(2020, 3, 6, 0, 0),\n",
       " datetime.datetime(2020, 3, 7, 0, 0),\n",
       " datetime.datetime(2020, 3, 8, 0, 0),\n",
       " datetime.datetime(2020, 3, 9, 0, 0),\n",
       " datetime.datetime(2020, 3, 10, 0, 0),\n",
       " datetime.datetime(2020, 3, 11, 0, 0),\n",
       " datetime.datetime(2020, 3, 12, 0, 0),\n",
       " datetime.datetime(2020, 3, 13, 0, 0),\n",
       " datetime.datetime(2020, 3, 14, 0, 0),\n",
       " datetime.datetime(2020, 3, 15, 0, 0),\n",
       " datetime.datetime(2020, 3, 16, 0, 0),\n",
       " datetime.datetime(2020, 3, 17, 0, 0),\n",
       " datetime.datetime(2020, 3, 18, 0, 0),\n",
       " datetime.datetime(2020, 3, 19, 0, 0),\n",
       " datetime.datetime(2020, 3, 20, 0, 0),\n",
       " datetime.datetime(2020, 3, 21, 0, 0),\n",
       " datetime.datetime(2020, 3, 22, 0, 0),\n",
       " datetime.datetime(2020, 3, 23, 0, 0),\n",
       " datetime.datetime(2020, 3, 24, 0, 0),\n",
       " datetime.datetime(2020, 3, 25, 0, 0),\n",
       " datetime.datetime(2020, 3, 26, 0, 0),\n",
       " datetime.datetime(2020, 3, 27, 0, 0),\n",
       " datetime.datetime(2020, 3, 28, 0, 0),\n",
       " datetime.datetime(2020, 3, 29, 0, 0),\n",
       " datetime.datetime(2020, 3, 30, 0, 0),\n",
       " datetime.datetime(2020, 3, 31, 0, 0),\n",
       " datetime.datetime(2020, 4, 1, 0, 0),\n",
       " datetime.datetime(2020, 4, 2, 0, 0),\n",
       " datetime.datetime(2020, 4, 3, 0, 0),\n",
       " datetime.datetime(2020, 4, 4, 0, 0),\n",
       " datetime.datetime(2020, 4, 5, 0, 0),\n",
       " datetime.datetime(2020, 4, 6, 0, 0),\n",
       " datetime.datetime(2020, 4, 7, 0, 0),\n",
       " datetime.datetime(2020, 4, 8, 0, 0),\n",
       " datetime.datetime(2020, 4, 9, 0, 0),\n",
       " datetime.datetime(2020, 4, 10, 0, 0),\n",
       " datetime.datetime(2020, 4, 11, 0, 0),\n",
       " datetime.datetime(2020, 4, 12, 0, 0),\n",
       " datetime.datetime(2020, 4, 13, 0, 0),\n",
       " datetime.datetime(2020, 4, 14, 0, 0),\n",
       " datetime.datetime(2020, 4, 15, 0, 0),\n",
       " datetime.datetime(2020, 4, 16, 0, 0),\n",
       " datetime.datetime(2020, 4, 17, 0, 0),\n",
       " datetime.datetime(2020, 4, 18, 0, 0),\n",
       " datetime.datetime(2020, 4, 19, 0, 0),\n",
       " datetime.datetime(2020, 4, 20, 0, 0),\n",
       " datetime.datetime(2020, 4, 21, 0, 0),\n",
       " datetime.datetime(2020, 4, 22, 0, 0),\n",
       " datetime.datetime(2020, 4, 23, 0, 0),\n",
       " datetime.datetime(2020, 4, 24, 0, 0),\n",
       " datetime.datetime(2020, 4, 25, 0, 0),\n",
       " datetime.datetime(2020, 4, 26, 0, 0),\n",
       " datetime.datetime(2020, 4, 27, 0, 0),\n",
       " datetime.datetime(2020, 4, 28, 0, 0),\n",
       " datetime.datetime(2020, 4, 29, 0, 0),\n",
       " datetime.datetime(2020, 4, 30, 0, 0),\n",
       " datetime.datetime(2020, 5, 1, 0, 0),\n",
       " datetime.datetime(2020, 5, 2, 0, 0),\n",
       " datetime.datetime(2020, 5, 3, 0, 0),\n",
       " datetime.datetime(2020, 5, 4, 0, 0),\n",
       " datetime.datetime(2020, 5, 5, 0, 0),\n",
       " datetime.datetime(2020, 5, 6, 0, 0),\n",
       " datetime.datetime(2020, 5, 7, 0, 0),\n",
       " datetime.datetime(2020, 5, 8, 0, 0),\n",
       " datetime.datetime(2020, 5, 9, 0, 0),\n",
       " datetime.datetime(2020, 5, 10, 0, 0),\n",
       " datetime.datetime(2020, 5, 11, 0, 0),\n",
       " datetime.datetime(2020, 5, 12, 0, 0),\n",
       " datetime.datetime(2020, 5, 13, 0, 0),\n",
       " datetime.datetime(2020, 5, 14, 0, 0),\n",
       " datetime.datetime(2020, 5, 15, 0, 0),\n",
       " datetime.datetime(2020, 5, 16, 0, 0),\n",
       " datetime.datetime(2020, 5, 17, 0, 0),\n",
       " datetime.datetime(2020, 5, 18, 0, 0),\n",
       " datetime.datetime(2020, 5, 19, 0, 0),\n",
       " datetime.datetime(2020, 5, 20, 0, 0),\n",
       " datetime.datetime(2020, 5, 21, 0, 0),\n",
       " datetime.datetime(2020, 5, 22, 0, 0),\n",
       " datetime.datetime(2020, 5, 23, 0, 0),\n",
       " datetime.datetime(2020, 5, 24, 0, 0),\n",
       " datetime.datetime(2020, 5, 25, 0, 0)]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daterange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "s=list(df['Total Confirmed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(daterange)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = adfuller(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -1.632138582011589\n",
      "p-value: 0.4663714932537098\n",
      "Critical Values:\n",
      "\t1%: -3.4954932834550623\n",
      "\t5%: -2.8900369024285117\n",
      "\t10%: -2.5819706975209726\n"
     ]
    }
   ],
   "source": [
    "print('ADF Statistic: {}'.format(result[0]))\n",
    "print('p-value: {}'.format(result[1]))\n",
    "print('Critical Values:')\n",
    "for key, value in result[4].items():\n",
    "    print('\\t{}: {}'.format(key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17569b742e8>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD4CAYAAAATpHZ6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAeDklEQVR4nO3deXhV1b3G8e+PDIQAYQwhjAmDzHNkkFoVh+uAohYr1qmtLWrr1No61FZre+11rNWOctU6ggMqTqigaK1VwIQ5hCEihAAZmAMh41n3j3PopUgk5Jxkn7Pzfp4nT3J2Ntm/9eyTl52111rbnHOIiIg/tPC6ABERiRyFuoiIjyjURUR8RKEuIuIjCnURER+Jb8qDde7c2WVkZDTlIUVEYl5OTs5251xqffZt0lDPyMggOzu7KQ8pIhLzzGxTffdV94uIiI8o1EVEfEShLiLiI0cNdTN70sxKzGzVIdseMLM1ZrbCzF4zs/aNW6aIiNRHfa7UnwLOPGzbfGCoc244sA64PcJ1iYhIAxw11J1zHwM7D9s2zzlXE3q5EOjRCLWJiMgxikSf+veBd+r6pplNN7NsM8suLS2NwOFERKQuYYW6md0B1ADP17WPc26Gcy7LOZeVmlqvsfMiIr5QXRtg0YYd3PvOGor3VjTJMRs8+cjMrgQmA6c6LcouIgLAgapaPlxbwtyV2/jH2lLKKmuIb2Ecn9GBtJSkRj9+g0LdzM4EbgVOcs6VR7YkEZHYUl5Vw4drSpm7chsL1pRwoLqWTq0TOXtYOqcMTGViv860TUpoklqOGupmNgs4GehsZoXAXQRHu7QE5psZwELn3DWNWKeISFSpqK7lo7UlvLliGx/kFVNRHaBzm0QuGN2dycPSGZvZkfi4pp8KdNRQd85dcoTNTzRCLSIiUe1gkL+9sogFecXsrwpekU8d04NzhnVjbGZH4lqYpzU26YJeIiKxpqomwII1Jby1YisL1pRQXlVLx9aJnDeyG2cPS2dCn06eXJHXRaEuInKY2oBj2ebdvLl8K68v28Ku8mo6tk5kysjunDMsnfF9vOlaqQ+FuogIweGHn6zfztsrt/HhmhJ27K8iMa4Fpw9JY+qYHpzYr3PUBvmhFOoi0mzVBhyLNuzgzRVbeWdVEbvLq0lJiueUgV04dVAaJx2XSrtWTTNqJVIU6iLSrOyrrOFf+dv5aG0J7+eVUFpWSXJiHKcPTuO8Ed04sX8qifHRf0VeF4W6iPheaVkl7+cV815uEZ/m76CqNkCblvGc2L8zk4d3Y9LALrRKjPO6zIhQqIuIL20o3ce81cXMX13MkoJdOAe9OiZz5Qm9mTQwjTG9O8T0FXldFOoi4gvOOXK37mXuym3MW11Mfsk+AIZ2T+GmU4/jjCFpDOzaltCESd9SqItITCvZW8HMxQW8umQLBTvLiWthjMvsyGXjenHa4DR6dEj2usQmpVAXkZhTUV3LP9dv5/VlW3h3VRE1AceJ/Tvz41P6csbgrnRoneh1iZ5RqItITKipDfBJ/nbmLN3C/NXBKfrtkxP47gkZXDa+NxmdW3tdYlRQqItIVPuidB8vZW/m1SVbKC2rpF2rBM4dEZqi37cTCTEwIagpKdRFJOrUBhzzVxfx5L82svjLncS1MCYN7MK3RnfnlIFdaBnvj+GHjUGhLiJRY+f+Kl5dUsjTn21k884D9OzYitvOGsiFo7vTpW3jP2DCDxTqIuK5VVv28Ld/fMF7uUVU1zqyenfgjrMHcfrgrp4vZRtrFOoi4pn8kjJ+P38dc1cWkZIUz6XjenPJ2F4M6NrW69JilkJdRJqUc47PN+5ixscbeD+vmNaJcdwwqR9Xndgn5hbPikYKdRFpEhXVtby5fCvPfLaJlVv20CE5gRtO7c+VE3rTqU1Lr8vzDYW6iDSqwl3lPLewgBc/L2BXeTX9u7Tht+cPZeroHr5ZRCuaKNRFJOL2HKjm/dXFvLViK/9YVwrA6YPTuPKEDCb06eT79Ve8pFAXkYgoq6jm/bxi3l6xjY/XbaeqNkD39q249uS+fGdcb7q3b+V1ic2CQl1EGuzgTc8XFhfw9sptVNYE6NYuicsn9Gby8HRG9myvq/ImplAXkWOWX1LGG8u28sbyrWzcUU7blvFclNWDC0b1YFTP9rTQ2HLPKNRFpF52l1fx5vKtzM4pZHnhHloYTOjbiR+d0o/Jw9NJTlScRIOjngUzexKYDJQ454aGtnUEXgQygI3At51zuxqvTBHxSsGOch7/ZAMvZW+mojrA4PQUfjV5MOeOSNfU/ShUn/9anwL+BDxzyLbbgA+cc/ea2W2h17dGvjwR8UJVTYAFa0qYnbOZBWtKiG/RgvNHdePKEzIY0q2d1+XJ1zhqqDvnPjazjMM2TwFODn39NPARCnWRmLenvJqnP9vIU59uZOf+Krq0bck1J/XlyhMySEvRVXksaGgnWJpzbhuAc26bmXWJYE0i0sTyS/Yxa3EBLywuYH9VLacO7MJlE3pzYr/OxGu98pjS6Hc2zGw6MB2gV69ejX04Eakn5xzv55Uw4+Mv+HzjLuJbGGcPS+fak/syKD3F6/KkgRoa6sVmlh66Sk8HSura0Tk3A5gBkJWV5Rp4PBGJkEDA8cGaEh75YB2rtuylV8dkbjtrIN8a3YPUtlqDJdY1NNTfAK4E7g19fj1iFYlIoyivquGVnEL+/q+NbNi+n14dk3lg6nAuGNVdXSw+Up8hjbMI3hTtbGaFwF0Ew/wlM7sKKAAuaswiRaThnHPMXVnEb99aTdHeCob3aMcj00Zy9rB0Pd/Th+oz+uWSOr51aoRrEZEIyy/Zx91v5vLP9dsZnJ7CI9NGMjazo6bu+5imgIn40J4D1Tz6wXqe/nQjrRLi+PW5g7lsfG91szQDCnURH9m4fT/PLtzES9mb2VdZw7Tje3LzGQPorIdQNBsKdREfWF8cfNbnO6uKiG9hnDm0K9ec1Jeh3TX7s7lRqIvEsPySffzlo3zmLN1CcmI810/qx+Xje9NFsz+bLYW6SAxatWUPf1qQz3uri0iMa8EPTuzDNSf1pWPrRK9LE48p1EViyK79VTwwby2zFhfQpmU8Pz65H9+dmKE+c/k3hbpIjHh31TZue3UlZRU1fO+ETG48rT/tWiV4XZZEGYW6SJSrDTgenr+OP32Yz4ge7bhv6nAGdtXaLHJkCnWRKLZpx35+9XouH68rZdrxPbl7yhBaxsd5XZZEMYW6SBTaub+KRz9Yz3MLN5EQ14L/Pn8ol47rpZmgclQKdZEoM3flNn45ZxV7DlRz8fE9uenU/hqiKPWmUBeJEnvKq/nl66t4c/lWhnVvx6wfjmdA17ZelyUxRqEuEgVyt+7h2ueWsHX3AX56+nFce3JfraAoDaJQF/HYS9mb+dWcVXRITuTFq8czpndHr0uSGKZQF/FIdW2Ae97O46lPNzKxXycemTZKk4gkbAp1EQ/s3F/FdTOX8OkXO/jBNzK57ayBWhZXIkKhLtKEnHO8sXwrv3lzNWWVNTx00Qi+NaaH12WJjyjURZpI0Z4Kbnt1BR+tLWVEz/bce+EwBqVrZqhElkJdpAnMyy3i1ldWUFEd4K5zB3PFhAziWmgikUSeQl2kEe2vrOGeuXnMXFTAkG4pPHrJKPqmtvG6LPExhbpII1m4YQc/n72cwl0HmP7NPtx8xnFat0UanUJdpBE8/s8N3DM3j14dk3np6gkcn6Gx59I0FOoiEfanBet5cN46zh7WlQcvGkFyon7NpOno3SYSIc4F1z1/dEE+F47qzv1Th2vsuTQ5hbpIhPztHxt4dEE+F2f15HcXDtPoFvFEWJcRZvYTM8s1s1VmNsvMtD6oNEuzFhdw37trOG9EN/5HgS4eanCom1l34AYgyzk3FIgDpkWqMJFY8e6qIu54bSUnD0jlwYtG0EKBLh4Kt8MvHmhlZvFAMrA1/JJEYseaor385MVlDO/Rnr9cOprEePWhi7ca/A50zm0BHgQKgG3AHufcvMP3M7PpZpZtZtmlpaUNr1Qkyuwpr+bqZ3NomxTPjMvHaJSLRIVwul86AFOATKAb0NrMLjt8P+fcDOdclnMuKzU1teGVikSRQMBx04tL2br7AH+9bLQeNydRI5y/FU8DvnTOlTrnqoFXgRMiU5ZIdPvzh/l8uLaUO88doodaSFQJJ9QLgPFmlmzBR5yfCuRFpiyR6LVoww4efn8dU0Z247JxvbwuR+Q/hNOnvgiYDSwBVoZ+1owI1SUSlXbsq+SGF5bSu1Nr7rlgGMHrGZHoEdadHefcXcBdEapFJKo55/jZy8vZVV7Nk989njYtdWNUoo/GX4nU0/OLCvhwbSm/OGsgQ7q187ockSNSqIvUw6Yd+/nd3DxO7N+ZKyZkeF2OSJ0U6iJHURtw3PzScuJaGPdPHa4ZoxLV1CkochSPffwF2Zt28ftvjyC9XSuvyxH5WrpSF/kan2/cyUPz1nHO8HQuGNXd63JEjkqhLlKHHfsquW7mEnp2aMW9F2r4osQGhbrIEdQGHD95KTh88c+XjqZtUoLXJYnUi0Jd5DDVtQFufGEpH68r5dfnDtHwRYkpulEqcojKmlqun7mUeauLuf2sgXxHywBIjFGoi4QU763gpheW8dmGHfz63MF8d2Km1yWJHDOFugjwzspt3P7aSiqrAzx00Qi+NaaH1yWJNIhCXZq1QMBx37treOzjDYzo0Y6HLx5Jn9Q2Xpcl0mAKdWm2qmsD3PrKCl5dsoXLx/fmznMHkxCnsQMS2xTq0ixV1wa4+tkcFqwp4ebTj+O6Sf00Dl18QaEuzY5zjrveyGXBmhJ+e/5QLh/f2+uSRCJGf2tKs/PMZ5uYuaiAa0/uq0AX31GoS7Py/upifvPWak4blMbPzxjgdTkiEafuF2kW9lfWcO87a3h24SYGp6fwh2kjtYSu+JJCXXwvd+sernkuh8JdB7jqG5n87IwBtEqM87oskUahUBdf+2T9dq55LoeUpHheunoCx2d09LokkUalUBffmrN0Cz97eTn9urThqe+NpWu7JK9LEml0CnXxHeccf/noCx54by0T+nTisSvGkKKlc6WZUKiLr9TUBrjzjVxmLipgyshu3D91OC3j1X8uzYdCXXzlV6/nMmtxcAz6z88YoBEu0uyENU7dzNqb2WwzW2NmeWY2IVKFiRyrOUu3MGtxAVef1IdbzxyoQJdmKdwr9UeAd51zU80sEUiOQE0ixyy/ZB+/eG0lx2d00KQiadYaHOpmlgJ8E/gugHOuCqiKTFki9VdeVcN1M5eQlBDHHy8ZTbxWWpRmLJx3fx+gFPi7mS01s8fNrPXhO5nZdDPLNrPs0tLSMA4n8lWBgOOnLy5nXXEZD188UsMWpdkLJ9TjgdHAX51zo4D9wG2H7+Scm+Gcy3LOZaWmpoZxOJGvenDeWt7NLeIXZw/ipOP0/hIJJ9QLgULn3KLQ69kEQ16kSby2tJC/fPQFl4ztxVXf0PNERSCMUHfOFQGbzezgXalTgdURqUrkKErKKrhzTi5jMzvymylD9IALkZBwR79cDzwfGvmyAfhe+CWJHN1976ylsibAfd8arkfQiRwirFB3zi0DsiJUi0i95GzayStLCvnRyX3J7PyVe/MizZoucSSm1AYcd76eS3q7JK6b1M/rckSijkJdYsrMRZvI3bqXO84ZRHKiVrkQOZxCXWJGSVkF97+3lon9OnHOsHSvyxGJSgp1iRm/ezuPyuoAv50yVKNdROqgUJeY8Gn+duYs28o1J/WhT2obr8sRiVoKdYl6O/ZVcsecVfTqmMyPTtHNUZGvo1CXqLZl9wEueuwztu4+wP1Th5OUoAdeiHwdDR+QqLWhdB+XPb6Isooanr1qHGMz9dBokaNRqEtUyi/ZxyX/u5BAwDFr+niGdm/ndUkiMUGhLlHnYKA7By9MH0//tLZelyQSMxTqElXWF5fxnccX4RzM+uE4BbrIMdKNUokaKwp38+3HPgMU6CINpSt1iQqffbGDHz6TTYfWCTx31Th6d9JCXSINoVAXTznneOKTL7n3nTVkdm7Ncz8YR1qKHkkn0lAKdfHMzv1V3DJ7Oe/nlfBfQ9K4f+oI2rVK8LoskZimUJcmVxtwzFxcwIPvreVAVS13nzeEKyb01nouIhGgUJcmtbaojJ++tIzcrXuZ0KcTd08ZwnG6ISoSMQp1aRLOOZ5bVMB/v7Watknx/PGSUUwenq6rc5EIU6hLo9tdXsUts1cwb3UxJx2XyoMXjSC1bUuvyxLxJYW6NKqcTTu5YdYySsoq+OU5g/j+xExatNDVuUhjUahLo3DO8fd/beSeuXl0b9+K2decwIie7b0uS8T3FOoScdW1Ae58PZdZiws4fXAaD317BClJGqoo0hQU6hJRpWWVXD9rCQs37ORHJ/flZ2cMUHeLSBNSqEvELNqwg+tnLWXPgWp+/+0RXDi6h9cliTQ7YYe6mcUB2cAW59zk8EuSWPRy9mZufWUFGZ1a8/T3xzIoPcXrkkSapUhcqd8I5AH6LW6mSsoquPvN1YzN7MjjVx5Pm5b6A1DEK2EtvWtmPYBzgMcjU47EogfeXUtlTS3/c+FwBbqIx8JdT/0PwC1AoK4dzGy6mWWbWXZpaWmYh5Nos3zzbl7OKeT7EzPJ7KzlckW81uBQN7PJQIlzLufr9nPOzXDOZTnnslJTUxt6OIlCzjl+/WYundu05LpJ/bwuR0QI70p9InCemW0EXgAmmdlzEalKYsJTn25kacFubjlzAG01Dl0kKjQ41J1ztzvnejjnMoBpwALn3GURq0yi2tKCXfxubh6nDerCVA1dFIkaekapHLPd5VVcN3MpaSlJPHTRSE0uEokiERmq4Jz7CPgoEj9LotuBqlqun7WUkrIKZl9zAu2S1e0iEk00/kzqrbSskh88k82Kwt3cd+FwLdAlEoUU6lIvq7bs4drncygtq+Sxy8ZwxpCuXpckIkegUJevtWt/FQ/NX8vMRQV0bN2SF6ZPYKSu0EWilkJd/oNzjoUbdvKPdaWsKNzN8s27qagJcMWEDH5y2nHqQxeJcgp1AYJh/nJOIU9+8iVrispIiDMGdk3hgtHduXx8BgO66uHQIrFAoS4AvLuqiFtmr2Bg17bcP3U4543oRlJCnNdlicgxUqgLAF+U7gNgzo8nKsxFYpgmHwkAxXsr6ZCcoEAXiXEKdQGgaG8FaSlJXpchImFSqAsAJXsr6KJQF4l5CnUBgt0vaW1bel2GiIRJoS7UBhyl+yrV/SLiAwp1Yce+SmoDjrR2CnWRWKdQF4r3VgKo+0XEBxTqQvHeCgB1v4j4gEJdKC5TqIv4hUJdKN5bSQuDzm0SvS5FRMKkUBeK91TQuU1L4uP0dhCJdfotForLNJtUxC8U6hKceJSikS8ifqBQFy0RIOIjCvVmrrKmlh37q0hrq1AX8QOFejNXWhaceNS1nbpfRPxAod7MHZxNqu4XEX9ocKibWU8z+9DM8sws18xujGRh0jRKDs4mVfeLiC+E8zi7GuBm59wSM2sL5JjZfOfc6gjVJk2g6N9LBKj7RcQPGnyl7pzb5pxbEvq6DMgDukeqMGkaxXsrSYgzOiRrNqmIH0SkT93MMoBRwKJI/DxpOiV7K+jSNokWLczrUkQkAsIOdTNrA7wC3OSc23uE7083s2wzyy4tLQ33cBJhwdmk6noR8YuwQt3MEggG+vPOuVePtI9zboZzLss5l5WamhrO4aQRBGeT6iapiF+EM/rFgCeAPOfc7yNXkjSVmtoARXu07ouIn4RzpT4RuByYZGbLQh9nR6guaWTOOX7z1mr2VdYwNrOj1+WISIQ0eEijc+4TQHfXYtQTn3zJM59t4ocnZnL2sHSvyxGRCNGM0mbog7xi7pmbx1lDu3L7WYO8LkdEIkih3gz99aMvyOzUmocvHqmhjCI+o1BvZrbvqySnYBfnjexGUkKc1+WISIQp1JuZBXklOAenD07zuhQRaQQK9WZm3uoiurdvxeD0FK9LEZFGoFBvRsqravjn+u2cPjiN4DQDEfEbhXoz8s/126msCajrRcTHFOrNyPzVxaQkxWuykYiPhbOeelQqq6hmf2Wt12VEHYdjwZoSThnYhYQ4/V8u4le+CvXivRWc9MCHVFQHvC4lap0xuKvXJYhII/JVqM/OKaSiOsCvJg8mOVFjsA/XKiGOM4cq1EX8zDehHgg4Xvx8MxP6dOKqb2R6XY6IiCd807m6cMMOCnaWc/HxPb0uRUTEM74J9RezN5OSFK/uBRFp1nwR6rvLq3hnVRHnj+qu9UxEpFnzRajPWbqFqpqAul5EpNnzRajPXVnEoPQUhnRr53UpIiKeivlQr6oJsLxwNyf07eR1KSIinov5UM/duofKmgBZvTt4XYqIiOdiPtRzNu0CYLRCXUQk9kN9ScEuenRoRVpKkteliIh4LqZD3TlHzqZdjNFVuogIEOOhXrjrAMV7KxXqIiIhMR3qSwqC/ekKdRGRoJgO9ZxNu2idGMeAtLZelyIiEhXCCnUzO9PM1ppZvpndFqmi6itn0y5G9mpPvB76ICIChBHqZhYH/Bk4CxgMXGJmgyNV2NHsq6whb9texvRS14uIyEHhrKc+Fsh3zm0AMLMXgCnA6kgUdqg/frCeN5Zv/Y9tlTUBAg7GZOh5myIiB4UT6t2BzYe8LgTGHb6TmU0HpgP06tWrQQdKbduS/mltvrJ9Yr9OjNNDlEVE/i2cULcjbHNf2eDcDGAGQFZW1le+Xx/TxvZi2tiG/YcgItKchHOHsRA4dK3bHsDWOvYVEZEmEE6ofw70N7NMM0sEpgFvRKYsERFpiAZ3vzjnaszsOuA9IA540jmXG7HKRETkmIXTp45zbi4wN0K1iIhImDRrR0TERxTqIiI+olAXEfERhbqIiI+Ycw2aD9Swg5mVApsa+M87A9sjWE408Fub/NYe8F+b/NYe8F+bjtSe3s651Pr84yYN9XCYWbZzLsvrOiLJb23yW3vAf23yW3vAf20Ktz3qfhER8RGFuoiIj8RSqM/wuoBG4Lc2+a094L82+a094L82hdWemOlTFxGRo4ulK3URETkKhbqIiI/ERKh7/YDrcJlZTzP70MzyzCzXzG4Mbe9oZvPNbH3oc0w9cNXM4sxsqZm9FXqdaWaLQu15MbQkc8wws/ZmNtvM1oTO1YRYPkdm9pPQ+22Vmc0ys6RYO0dm9qSZlZjZqkO2HfGcWNCjoZxYYWajvau8bnW06YHQ+26Fmb1mZu0P+d7toTatNbP/OtrPj/pQ9/oB1xFSA9zsnBsEjAd+HGrDbcAHzrn+wAeh17HkRiDvkNf3AQ+H2rMLuMqTqhruEeBd59xAYATBtsXkOTKz7sANQJZzbijB5bGnEXvn6CngzMO21XVOzgL6hz6mA39tohqP1VN8tU3zgaHOueHAOuB2gFBOTAOGhP7NX0KZWKeoD3UOecC1c64KOPiA65jhnNvmnFsS+rqMYFh0J9iOp0O7PQ2c702Fx87MegDnAI+HXhswCZgd2iXW2pMCfBN4AsA5V+Wc200MnyOCS2u3MrN4IBnYRoydI+fcx8DOwzbXdU6mAM+4oIVAezNLb5pK6+9IbXLOzXPO1YReLiT4JDkItukF51ylc+5LIJ9gJtYpFkL9SA+47u5RLWEzswxgFLAISHPObYNg8ANdvKvsmP0BuAUIhF53AnYf8saMtfPUBygF/h7qUnrczFoTo+fIObcFeBAoIBjme4AcYvscHVTXOfFLVnwfeCf09TG3KRZCvV4PuI4FZtYGeAW4yTm31+t6GsrMJgMlzrmcQzcfYddYOk/xwGjgr865UcB+YqSr5UhC/cxTgEygG9CaYPfE4WLpHB1NrL8HMbM7CHbXPn9w0xF2+9o2xUKo++IB12aWQDDQn3fOvRraXHzwz8PQ5xKv6jtGE4HzzGwjwe6wSQSv3NuH/tSH2DtPhUChc25R6PVsgiEfq+foNOBL51ypc64aeBU4gdg+RwfVdU5iOivM7EpgMnCp+/8JRMfcplgI9Zh/wHWov/kJIM859/tDvvUGcGXo6yuB15u6toZwzt3unOvhnMsgeD4WOOcuBT4EpoZ2i5n2ADjnioDNZjYgtOlUYDUxeo4IdruMN7Pk0PvvYHti9hwdoq5z8gZwRWgUzHhgz8FummhnZmcCtwLnOefKD/nWG8A0M2tpZpkEbwIv/tof5pyL+g/gbIJ3hL8A7vC6ngbU/w2CfzKtAJaFPs4m2A/9AbA+9Lmj17U2oG0nA2+Fvu4TesPlAy8DLb2u7xjbMhLIDp2nOUCHWD5HwN3AGmAV8CzQMtbOETCL4D2BaoJXrVfVdU4IdlX8OZQTKwmO/PG8DfVsUz7BvvOD+fC3Q/a/I9SmtcBZR/v5WiZARMRHYqH7RURE6kmhLiLiIwp1EREfUaiLiPiIQl1ExEcU6iIiPqJQFxHxkf8DdT4ULE7lJQUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_log = np.log(s)\n",
    "plt.plot(df_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17569ec1320>]"
      ]
     },
     "execution_count": 41,
     "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": [
    "plt.plot(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "You must specify a period or x must be a pandas object with a DatetimeIndex with a freq not set to None",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-42-d409a77d21dd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdecomposition\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseasonal_decompose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_log\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      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mARIMA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_log\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\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      3\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdisp\u001b[0m\u001b[1;33m=\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      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_log_shift\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfittedvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'red'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\bharg\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    206\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    207\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\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[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\bharg\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\statsmodels\\tsa\\seasonal.py\u001b[0m in \u001b[0;36mseasonal_decompose\u001b[1;34m(x, model, filt, period, two_sided, extrapolate_trend)\u001b[0m\n\u001b[0;32m    140\u001b[0m             \u001b[0mperiod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpfreq\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\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;32m--> 142\u001b[1;33m             raise ValueError(\"You must specify a period or x must be a \"\n\u001b[0m\u001b[0;32m    143\u001b[0m                              \u001b[1;34m\"pandas object with a DatetimeIndex with \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m                              \"a freq not set to None\")\n",
      "\u001b[1;31mValueError\u001b[0m: You must specify a period or x must be a pandas object with a DatetimeIndex with a freq not set to None"
     ]
    }
   ],
   "source": [
    "decomposition = seasonal_decompose(df_log) \n",
    "model = ARIMA(df_log, order=(2,1,2))\n",
    "results = model.fit(disp=-1)\n",
    "plt.plot(df_log_shift)\n",
    "plt.plot(results.fittedvalues, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(s, index =daterange, columns =['Cases'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-02</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-03</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cases\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"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": [
    "rolling_mean = df.rolling(window = 12).mean()\n",
    "rolling_std = df.rolling(window = 12).std()\n",
    "plt.plot(df, color = 'blue', label = 'Original')\n",
    "plt.plot(rolling_mean, color = 'red', label = 'Rolling Mean')\n",
    "plt.plot(rolling_std, color = 'black', label = 'Rolling Std')\n",
    "plt.legend(loc = 'best')\n",
    "plt.title('Rolling Mean & Rolling Standard Deviation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1756a48d6a0>]"
      ]
     },
     "execution_count": 80,
     "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": [
    "df_log = np.log(df)\n",
    "plt.plot(df_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stationarity(timeseries):\n",
    "    \n",
    "    # rolling statistics\n",
    "    rolling_mean = timeseries.rolling(window=12).mean()\n",
    "    rolling_std = timeseries.rolling(window=12).std()\n",
    "    \n",
    "    # rolling statistics plot\n",
    "    original = plt.plot(timeseries, color='blue', label='Original')\n",
    "    mean = plt.plot(rolling_mean, color='red', label='Rolling Mean')\n",
    "    std = plt.plot(rolling_std, color='black', label='Rolling Std')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Rolling Mean & Standard Deviation')\n",
    "    plt.show(block=False)\n",
    "    \n",
    "    # Dickey–Fuller test:\n",
    "    result = adfuller(timeseries['Cases'])\n",
    "    print('ADF Statistic: {}'.format(result[0]))\n",
    "    print('p-value: {}'.format(result[1]))\n",
    "    print('Critical Values:')\n",
    "    for key, value in result[4].items():\n",
    "        print('\\t{}: {}'.format(key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -2.6066012691084817\n",
      "p-value: 0.09164058037075723\n",
      "Critical Values:\n",
      "\t1%: -3.4954932834550623\n",
      "\t5%: -2.8900369024285117\n",
      "\t10%: -2.5819706975209726\n"
     ]
    }
   ],
   "source": [
    "rolling_mean = df_log.rolling(window=12).mean()\n",
    "df_log_minus_mean = df_log - rolling_mean\n",
    "df_log_minus_mean.dropna(inplace=True)\n",
    "get_stationarity(df_log_minus_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -1.8107657530589232\n",
      "p-value: 0.3751300470531914\n",
      "Critical Values:\n",
      "\t1%: -3.489057523907491\n",
      "\t5%: -2.887246327182993\n",
      "\t10%: -2.5804808802708528\n"
     ]
    }
   ],
   "source": [
    "rolling_mean_exp_decay = df_log.ewm(halflife=12, min_periods=0, adjust=True).mean()\n",
    "df_log_exp_decay = df_log - rolling_mean_exp_decay\n",
    "df_log_exp_decay.dropna(inplace=True)\n",
    "get_stationarity(df_log_exp_decay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -5.567453794715336\n",
      "p-value: 1.4935288148849653e-06\n",
      "Critical Values:\n",
      "\t1%: -3.489057523907491\n",
      "\t5%: -2.887246327182993\n",
      "\t10%: -2.5804808802708528\n"
     ]
    }
   ],
   "source": [
    "df_log_shift = df_log - df_log.shift()\n",
    "df_log_shift.dropna(inplace=True)\n",
    "get_stationarity(df_log_shift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.tsa.seasonal.DecomposeResult at 0x1756a732dd8>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasonal_decompose(df['Cases'],model ='multiplicative') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\bharg\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "c:\\users\\bharg\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  % freq, ValueWarning)\n",
      "c:\\users\\bharg\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1756de285f8>]"
      ]
     },
     "execution_count": 92,
     "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": [
    "model = ARIMA(df_log, order=(3,1,3))\n",
    "results = model.fit(disp=-1)\n",
    "plt.plot(df_log_shift)\n",
    "plt.plot(results.fittedvalues, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1756a3be7b8>]"
      ]
     },
     "execution_count": 93,
     "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": [
    "predictions_ARIMA_diff = pd.Series(results.fittedvalues, copy=True)\n",
    "predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff.cumsum()\n",
    "predictions_ARIMA_log = pd.Series(df_log['Cases'].iloc[0], index=df_log.index)\n",
    "predictions_ARIMA_log = predictions_ARIMA_log.add(predictions_ARIMA_diff_cumsum, fill_value=0)\n",
    "predictions_ARIMA = np.exp(predictions_ARIMA_log)\n",
    "plt.plot(df)\n",
    "plt.plot(predictions_ARIMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}