final_predictor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from google.cloud import bigquery\n",
    "\n",
    "import plotly  \n",
    "import plotly.graph_objs as go\n",
    "import scipy.stats\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection   import train_test_split\n",
    "from sklearn import linear_model as lm\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import tscv as cv\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from statsmodels.tsa.api import Holt,SimpleExpSmoothing,ExponentialSmoothing\n",
    "from fbprophet import Prophet\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from pmdarima.arima import auto_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The google.cloud.bigquery extension is already loaded. To reload it, use:\n",
      "  %reload_ext google.cloud.bigquery\n"
     ]
    }
   ],
   "source": [
    "%load_ext google.cloud.bigquery\n",
    "client = bigquery.Client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = \"\"\"SELECT date, location_key, country_code, country_name, aggregation_level, new_confirmed, \n",
    "        new_deceased, new_recovered, new_tested, cumulative_confirmed, cumulative_deceased,\n",
    "        cumulative_recovered, cumulative_tested, new_hospitalized_patients, new_intensive_care_patients, \n",
    "        average_temperature_celsius, minimum_temperature_celsius, maximum_temperature_celsius \n",
    "        from covid19_open_data.covid19_open_data where location_key=country_code\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = client.query(sql).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_US_sim = df[df['location_key'] == 'US']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-0.31"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_US_sim['daily_change'] = df_US_sim.new_confirmed.pct_change()\n",
    "round(df_US_sim['daily_change'], 2).quantile(0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print(test_lst)\n",
    "test_lst = list(df_US_sim['daily_change'])\n",
    "new_test_lst = []\n",
    "for items in test_lst:\n",
    "    if items == float('nan') or items == float('inf'):\n",
    "        new_test_lst.append(0.0)\n",
    "    else:\n",
    "        new_test_lst.append(items)\n",
    "df_US_sim['daily_change'] = new_test_lst\n",
    "df_US_sim['daily_change'] = df_US_sim['daily_change'].fillna(0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x595.44 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.despine(left=True)\n",
    "\n",
    "#plot a simple histogram with binsize determined automatically\n",
    "# plt.Figure(figsize=(20,10))\n",
    "a4_dims = (20, 8.27)\n",
    "fig, ax = plt.subplots(figsize=a4_dims)\n",
    "sns.distplot(df_US_sim['daily_change'], color=\"green\")\n",
    "plt.title('confirmed cases')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With 95% confidence, the daily increase in Covid cases will not exceed 31% of the previous day's confirmed cases\n",
    "\n",
    "### If we have 10,000 cases today, our one-day 31% VaR is 0.31 * 10,000 = 3100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_US_sim_ind = df_US_sim.set_index('date')\n",
    "df_US_sim_ind['daily_change'].plot(figsize = (20, 10), color=\"green\", grid = True)\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"percentage change\")\n",
    "plt.title(\"Date VS percentage change in confirmed cases\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "## Adding Weeks, Months and Year Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_US_sim_ind.index = pd.to_datetime(df_US_sim_ind.index, errors='coerce')\n",
    "df_US_sim_ind['day_of_week'] = df_US_sim_ind.index.dayofweek\n",
    "df_US_sim_ind['day_of_month'] = df_US_sim_ind.index.day\n",
    "df_US_sim_ind['quarter'] = df_US_sim_ind.index.quarter\n",
    "df_US_sim_ind['month'] = df_US_sim_ind.index.month\n",
    "df_US_sim_ind['year'] = df_US_sim_ind.index.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Summary of the values at previous Time Steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [],
   "source": [
    "width = 3\n",
    "shifted = df_US_sim_ind['new_confirmed'].shift(width - 1)\n",
    "roll_window = shifted.rolling(window=width)\n",
    "df_US_sim_ind['min'] = roll_window.min()\n",
    "df_US_sim_ind['mean'] = roll_window.mean()\n",
    "df_US_sim_ind['max'] = roll_window.max()\n",
    "# df_US_sim_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pred = df_pred.drop(columns=['prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pred = df_US_sim_ind.copy()\n",
    "n = int(30)\n",
    "future_pred = n\n",
    "df_pred = df_pred[:-2]\n",
    "# Create another column (the target/ dependent variable) shifted 'n' units up\n",
    "df_pred['prediction'] = df_pred['cumulative_confirmed'][:-n]\n",
    "df_pred['prediction'] = df_pred['prediction'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['location_key', 'country_code', 'country_name', 'aggregation_level',\n",
       "       'new_confirmed', 'new_deceased', 'new_tested', 'cumulative_confirmed',\n",
       "       'cumulative_deceased', 'cumulative_tested', 'new_hospitalized_patients',\n",
       "       'new_intensive_care_patients', 'average_temperature_celsius',\n",
       "       'minimum_temperature_celsius', 'maximum_temperature_celsius',\n",
       "       'daily_change', 'day_of_week', 'day_of_month', 'quarter', 'month',\n",
       "       'year', 'min', 'mean', 'max', 'prediction'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 500,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pred = df_pred.drop(columns=['cumulative_recovered', 'new_recovered'])\n",
    "df_pred = df_pred.fillna(0)\n",
    "df_pred.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(df_pred.drop(columns = ['location_key','country_code','country_name','aggregation_level'\n",
    "                                    ,'new_deceased','cumulative_confirmed','cumulative_deceased','cumulative_tested'\n",
    "                                    ,'minimum_temperature_celsius',\n",
    "                                   'maximum_temperature_celsius', 'daily_change', 'day_of_week',\n",
    "                                   'day_of_month', 'quarter', 'month', 'year', 'min', 'mean', 'max',\n",
    "                                   'prediction'],axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_sim = df_pred.drop(columns = ['location_key','country_code','country_name','aggregation_level'\n",
    "#                                     ,'new_deceased','cumulative_confirmed','cumulative_deceased','cumulative_tested'\n",
    "#                                     ,'minimum_temperature_celsius',\n",
    "#                                    'maximum_temperature_celsius', 'daily_change', 'day_of_week',\n",
    "#                                    'day_of_month', 'quarter', 'month', 'year', 'min', 'mean', 'max',\n",
    "#                                    'prediction'],axis=1)\n",
    "# X_sim.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288, 5)"
      ]
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_forecast = X[-future_pred:]\n",
    "X = X[:-future_pred]\n",
    "y = np.array(df_pred['prediction'])\n",
    "y = y[:-future_pred]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time series length:  201\n",
      "Number of time series:  4\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
    "\n",
    "print(\"Time series length: \",len(X_train))\n",
    "print(\"Number of time series: \", len(X_train[0,1:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time-Series Cross Validation (tscv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = cv.GapKFold(n_splits=2, gap_before=1, gap_after=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=-1, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = xgb.XGBRegressor(objective = 'reg:squarederror', n_jobs=-1)\n",
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence %:  86.7\n",
      "\n",
      "\n",
      "\u001b[4mExpected confirmed cases for next 30 days\u001b[0m\n",
      "[6257404.5 5606279.  7532021.  6439852.5 7810584.5 8012638.  7700342.5\n",
      " 6342195.5 5868985.5 5042815.5 7091149.  6193865.5 6171198.  6160367.\n",
      " 6160208.5 5905254.  5603132.  5424311.  5905254.  6148989.5 6145171.\n",
      " 5998998.  5929891.  5089190.  6289329.5 2454202.2  913761.4  913761.4\n",
      "  913761.4  913761.4]\n",
      "\n",
      "\n",
      "Test RMSE: 4460852.297\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "confidence = reg.score(X_test, y_test)\n",
    "print(\"Confidence %: \", round(confidence*100,2))\n",
    "print('\\n')\n",
    "prediction = reg.predict(X_forecast)\n",
    "print('\\033[4mExpected confirmed cases for next 30 days\\033[0m')\n",
    "prediction = prediction\n",
    "print(prediction)\n",
    "print('\\n')\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test[:future_pred], prediction[:future_pred]))\n",
    "print('Test RMSE: %.3f' % rmse)\n",
    "print('\\n')\n",
    "model_scores = []\n",
    "model_scores.append(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1 = lm.LinearRegression()\n",
    "reg1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence %:  82.61\n",
      "\n",
      "\n",
      "\u001b[4mExpected confirmed cases for next 30 days\u001b[0m\n",
      "[7594780.17598493 6379920.50455891 4271312.26130473 5055516.69960978\n",
      " 7225110.89745591 8390311.64339575 8820920.15666624 8497781.69433719\n",
      " 6889976.28522412 4753287.2623204  5699952.46203626 7870011.68619044\n",
      " 8615270.66026932 8548285.13227718 8679111.06060927 6962267.56410754\n",
      " 5129235.47791581 5718137.22831268 7017863.77968465 7992295.87538833\n",
      " 7874564.6994613  7287238.15077378 6432504.17290699 4183362.03925393\n",
      " 3955612.86390965 2471264.24277758 -341232.62244235 -307782.27570585\n",
      " -344245.12099899 -413807.74920504]\n",
      "\n",
      "\n",
      "Test RMSE: 5125787.049\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "confidence1 = reg1.score(X_test, y_test)\n",
    "print(\"Confidence %: \", round(confidence1*100,2))\n",
    "print('\\n')\n",
    "prediction1 = reg1.predict(X_forecast)\n",
    "print('\\033[4mExpected confirmed cases for next 30 days\\033[0m')\n",
    "print(prediction1)\n",
    "print('\\n')\n",
    "\n",
    "rmse1 = np.sqrt(mean_squared_error(y_test[:future_pred], prediction1[:future_pred]))\n",
    "print('Test RMSE: %.3f' % rmse1)\n",
    "print('\\n')\n",
    "model_scores.append(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Randon Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=4, random_state=0)"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = RandomForestRegressor(max_depth=4, random_state=0)\n",
    "regr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence %:  87.44\n",
      "\n",
      "\n",
      "\u001b[4mExpected confirmed cases for next 30 days\u001b[0m\n",
      "[6194500.76314416 5917461.63104854 6450597.12636997 6655556.96118793\n",
      " 6825272.69315    6901496.29737222 6682746.31885049 6193830.53314416\n",
      " 6103475.7941851  5566536.54248226 6223236.65710395 6130218.36692194\n",
      " 6127076.85092194 6127076.85092194 6193830.53314416 6184360.61114416\n",
      " 5633290.22470448 5644338.50887115 6184360.61114416 6197838.72014416\n",
      " 6197838.72014416 6200980.23614416 6113129.84340732 5622977.35662367\n",
      " 5622977.35662367 2829996.81830876  592054.09347578  589792.19514245\n",
      "  592054.09347578  592054.09347578]\n",
      "\n",
      "\n",
      "Test RMSE: 4363632.711\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "confidence3 = regr.score(X_test, y_test)\n",
    "print(\"Confidence %: \", round(confidence3*100,2))\n",
    "print('\\n')\n",
    "prediction3 = regr.predict(X_forecast)\n",
    "print('\\033[4mExpected confirmed cases for next 30 days\\033[0m')\n",
    "print(prediction3)\n",
    "print('\\n')\n",
    "\n",
    "rmse3 = np.sqrt(mean_squared_error(y_test[:future_pred], prediction3[:future_pred]))\n",
    "print('Test RMSE: %.3f' % rmse3)\n",
    "print('\\n')\n",
    "model_scores.append(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping the rows at the beginning of this year which has 0 cumulative confirmed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "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>cumulative_confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20177</th>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20178</th>\n",
       "      <td>2020-01-19</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20179</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20180</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20181</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  cumulative_confirmed\n",
       "20177  2020-01-18                   5.0\n",
       "20178  2020-01-19                   5.0\n",
       "20179  2020-01-20                   1.0\n",
       "20180  2020-01-21                   1.0\n",
       "20181  2020-01-22                   1.0"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_US_sim_ARM = df_US_sim[['date','cumulative_confirmed']]\n",
    "df_US_sim_ARM = df_US_sim_ARM[:-2] # dropping today and yesterday rows/values\n",
    "df_US_sim_ARM = df_US_sim_ARM.loc[~(df_US_sim_ARM['cumulative_confirmed'] == 0.0)]\n",
    "df_US_sim_ARM.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Holts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-11-14\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "\n",
    "valid_set = df_US_sim_ARM[-31:]\n",
    "train_set = df_US_sim_ARM[:-31]\n",
    "\n",
    "begin_date = valid_set.iloc[[-1]].date\n",
    "begin_date = begin_date + timedelta(days=1)\n",
    "begin_date = begin_date.to_string()[9:]\n",
    "print(begin_date)\n",
    "# begin_date = '2020-11-14'\n",
    "df_date = pd.DataFrame({'cumulative_confirmed':0, \n",
    "                      'date':pd.date_range(begin_date, periods=90)})\n",
    "test_set = valid_set.append(df_date, ignore_index=True)\n",
    "test_set['date'] = pd.to_datetime(test_set['date'] ,errors = 'coerce',format = '%Y-%m-%d')\n",
    "test_set = test_set.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_train = train_set.set_index('date')  # only training set without last 30 rows\n",
    "valid = valid_set.set_index('date') # only validation set with only last 30 rows\n",
    "y_pred=test_set.copy() # validation set + test set with last 30 rows + additional 90 rows/dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [],
   "source": [
    "holt=Holt(np.asarray(model_train[\"cumulative_confirmed\"])).fit(smoothing_level=0.3, smoothing_slope=0.3,optimized=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error Holt's Linear Model:  510568.08911942056\n"
     ]
    }
   ],
   "source": [
    "\n",
    "y_pred[\"Holt\"]=holt.forecast(len(test_set))\n",
    "model_scores.append(np.sqrt(mean_squared_error(y_pred[\"cumulative_confirmed\"][:valid.shape[0]-1],y_pred[\"Holt\"][:valid.shape[0]-1])))\n",
    "print(\"Root Mean Square Error Holt's Linear Model: \",np.sqrt(mean_squared_error(y_pred[\"cumulative_confirmed\"][:valid.shape[0]-1],y_pred[\"Holt\"][:valid.shape[0]-1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "mode": "lines+markers",
         "name": "Train Data for Confirmed Cases",
         "type": "scatter",
         "x": [
          "2020-01-18",
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   ],
   "source": [
    "fig=go.Figure()\n",
    "fig.add_trace(go.Scatter(x=model_train.index, y=model_train[\"cumulative_confirmed\"],\n",
    "                    mode='lines+markers',name=\"Train Data for Confirmed Cases\"))\n",
    "fig.add_trace(go.Scatter(x=valid.index, y=valid[\"cumulative_confirmed\"],\n",
    "                    mode='lines+markers',name=\"Validation Data for Confirmed Cases\",))\n",
    "fig.add_trace(go.Scatter(x=y_pred.index, y=y_pred[\"Holt\"],\n",
    "                    mode='lines+markers',name=\"Prediction of Confirmed Cases\",))\n",
    "fig.update_layout(title=\"Confirmed Cases Holt's Linear Model Prediction\",\n",
    "                 xaxis_title=\"Date\",yaxis_title=\"Confirmed Cases\",legend=dict(x=0,y=1,traceorder=\"normal\"))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Holt's Winter Model for Daily Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_train['cumulative_confirmed_winter'] = model_train['cumulative_confirmed'].astype(int)\n",
    "model_train['cumulative_confirmed_winter'] = model_train['cumulative_confirmed'].abs()\n",
    "# model_train['cumulative_confirmed_winter'] = model_train['cumulative_confirmed'].replace(0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cumulative_confirmed</th>\n",
       "      <th>cumulative_confirmed_winter</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>2020-01-18</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-19</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>7664676.0</td>\n",
       "      <td>7664676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-10</th>\n",
       "      <td>7718947.0</td>\n",
       "      <td>7718947.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-11</th>\n",
       "      <td>7762544.0</td>\n",
       "      <td>7762544.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-12</th>\n",
       "      <td>7804197.0</td>\n",
       "      <td>7804197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>7856714.0</td>\n",
       "      <td>7856714.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cumulative_confirmed  cumulative_confirmed_winter\n",
       "date                                                         \n",
       "2020-01-18                   5.0                          5.0\n",
       "2020-01-19                   5.0                          5.0\n",
       "2020-01-20                   1.0                          1.0\n",
       "2020-01-21                   1.0                          1.0\n",
       "2020-01-22                   1.0                          1.0\n",
       "...                          ...                          ...\n",
       "2020-10-09             7664676.0                    7664676.0\n",
       "2020-10-10             7718947.0                    7718947.0\n",
       "2020-10-11             7762544.0                    7762544.0\n",
       "2020-10-12             7804197.0                    7804197.0\n",
       "2020-10-13             7856714.0                    7856714.0\n",
       "\n",
       "[270 rows x 2 columns]"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/statsmodels/tsa/holtwinters.py:744: ConvergenceWarning:\n",
      "\n",
      "Optimization failed to converge. Check mle_retvals.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "es=ExponentialSmoothing(np.asarray(model_train['cumulative_confirmed_winter']),seasonal_periods=7,trend='add', seasonal='add').fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error for Holt's Winter Model:  461543.8620901662\n"
     ]
    }
   ],
   "source": [
    "y_pred[\"Holt's Winter Model\"]=es.forecast(len(test_set))\n",
    "model_scores.append(np.sqrt(mean_squared_error(y_pred[\"cumulative_confirmed\"][:valid.shape[0]-1],y_pred[\"Holt's Winter Model\"][:valid.shape[0]-1])))\n",
    "print(\"Root Mean Square Error for Holt's Winter Model: \",np.sqrt(mean_squared_error(y_pred[\"cumulative_confirmed\"][:valid.shape[0]-1],y_pred[\"Holt's Winter Model\"][:valid.shape[0]-1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>cumulative_confirmed</th>\n",
       "      <th>cumulative_confirmed_winter</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2020-01-18</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-19</th>\n",
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       "      <td>7664676.0</td>\n",
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       "      <td>7718947.0</td>\n",
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       "      <th>2020-10-11</th>\n",
       "      <td>7762544.0</td>\n",
       "      <td>7762544.0</td>\n",
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       "      <th>2020-10-12</th>\n",
       "      <td>7804197.0</td>\n",
       "      <td>7804197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>7856714.0</td>\n",
       "      <td>7856714.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cumulative_confirmed  cumulative_confirmed_winter\n",
       "date                                                         \n",
       "2020-01-18                   5.0                          5.0\n",
       "2020-01-19                   5.0                          5.0\n",
       "2020-01-20                   1.0                          1.0\n",
       "2020-01-21                   1.0                          1.0\n",
       "2020-01-22                   1.0                          1.0\n",
       "...                          ...                          ...\n",
       "2020-10-09             7664676.0                    7664676.0\n",
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       "2020-10-12             7804197.0                    7804197.0\n",
       "2020-10-13             7856714.0                    7856714.0\n",
       "\n",
       "[270 rows x 2 columns]"
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     "execution_count": 523,
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   "cell_type": "code",
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   ],
   "source": [
    "fig=go.Figure()\n",
    "fig.add_trace(go.Scatter(x=model_train.index, y=model_train[\"cumulative_confirmed_winter\"],\n",
    "                    mode='lines+markers',name=\"Train Data for Confirmed Cases\"))\n",
    "fig.add_trace(go.Scatter(x=valid.index, y=valid[\"cumulative_confirmed\"],\n",
    "                    mode='lines+markers',name=\"Validation Data for Confirmed Cases\",))\n",
    "fig.add_trace(go.Scatter(x=y_pred.index, y=y_pred[\"Holt's Winter Model\"],\n",
    "                    mode='lines+markers',name=\"Prediction of Confirmed Cases\",))\n",
    "fig.update_layout(title=\"Confirmed Cases Holt's Winter Model Prediction\",\n",
    "                 xaxis_title=\"Date\",yaxis_title=\"Confirmed Cases\",legend=dict(x=0,y=1,traceorder=\"normal\"))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred[\"Holt's Winter Model\"] = y_pred[\"Holt's Winter Model\"].astype(int)\n",
    "y_pred[\"Holt\"] = y_pred[\"Holt\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cumulative_confirmed</th>\n",
       "      <th>Holt</th>\n",
       "      <th>Holt's Winter Model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-14</th>\n",
       "      <td>7916100.0</td>\n",
       "      <td>7906176</td>\n",
       "      <td>7908379</td>\n",
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       "      <th>2020-10-15</th>\n",
       "      <td>7979885.0</td>\n",
       "      <td>7956504</td>\n",
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       "      <th>2020-10-16</th>\n",
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       "      <th>2020-10-17</th>\n",
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       "      <td>8057159</td>\n",
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       "      <th>2020-10-18</th>\n",
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       "      <td>8107486</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13744163</td>\n",
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       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
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       "      <td>13794491</td>\n",
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       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13844818</td>\n",
       "      <td>14152654</td>\n",
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       "    <tr>\n",
       "      <th>2021-02-10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13895146</td>\n",
       "      <td>14204876</td>\n",
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       "    <tr>\n",
       "      <th>2021-02-11</th>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "<p>121 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "            cumulative_confirmed      Holt  Holt's Winter Model\n",
       "date                                                           \n",
       "2020-10-14             7916100.0   7906176              7908379\n",
       "2020-10-15             7979885.0   7956504              7968196\n",
       "2020-10-16             8050141.0   8006831              8033561\n",
       "2020-10-17             8106752.0   8057159              8088992\n",
       "2020-10-18             8154595.0   8107486              8131150\n",
       "...                          ...       ...                  ...\n",
       "2021-02-07                   0.0  13744163             14057265\n",
       "2021-02-08                   0.0  13794491             14101790\n",
       "2021-02-09                   0.0  13844818             14152654\n",
       "2021-02-10                   0.0  13895146             14204876\n",
       "2021-02-11                   0.0  13945473             14264694\n",
       "\n",
       "[121 rows x 3 columns]"
      ]
     },
     "execution_count": 526,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AR Model (using AUTO ARIMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ARIMA(0,2,0)(0,0,0)[0] intercept   : AIC=5440.501, Time=0.01 sec\n",
      " ARIMA(1,2,0)(0,0,0)[0] intercept   : AIC=5425.069, Time=0.02 sec\n",
      " ARIMA(2,2,0)(0,0,0)[0] intercept   : AIC=5416.252, Time=0.04 sec\n",
      " ARIMA(3,2,0)(0,0,0)[0] intercept   : AIC=5403.096, Time=0.11 sec\n",
      " ARIMA(4,2,0)(0,0,0)[0] intercept   : AIC=5398.005, Time=0.13 sec\n",
      "Total fit time: 0.320 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ARIMA(order=(4, 2, 0), scoring_args={}, suppress_warnings=True)"
      ]
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ar= auto_arima(model_train[\"cumulative_confirmed\"],trace=True, error_action='ignore', start_p=0,start_q=0,max_p=4,max_q=0,\n",
    "                   suppress_warnings=True,stepwise=False,seasonal=False)\n",
    "model_ar.fit(model_train[\"cumulative_confirmed\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_ar=model_ar.predict(len(test_set))\n",
    "y_pred[\"AR Model Prediction\"]=prediction_ar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error for AR Model:  469409.9975181326\n"
     ]
    }
   ],
   "source": [
    "model_scores.append(np.sqrt(mean_squared_error(y_pred[\"cumulative_confirmed\"][:valid.shape[0]-1],y_pred[\"AR Model Prediction\"][:valid.shape[0]-1])))\n",
    "print(\"Root Mean Square Error for AR Model: \",np.sqrt(mean_squared_error(y_pred[\"cumulative_confirmed\"][:valid.shape[0]-1],y_pred[\"AR Model Prediction\"][:valid.shape[0]-1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "fig=go.Figure()\n",
    "fig.add_trace(go.Scatter(x=model_train.index, y=model_train[\"cumulative_confirmed\"],\n",
    "                    mode='lines+markers',name=\"Train Data for Confirmed Cases\"))\n",
    "fig.add_trace(go.Scatter(x=valid.index, y=valid[\"cumulative_confirmed\"],\n",
    "                    mode='lines+markers',name=\"Validation Data for Confirmed Cases\",))\n",
    "fig.add_trace(go.Scatter(x=y_pred.index, y=y_pred[\"AR Model Prediction\"],\n",
    "                    mode='lines+markers',name=\"Prediction of Confirmed Cases\",))\n",
    "fig.update_layout(title=\"Confirmed Cases AR Model Prediction\",\n",
    "                 xaxis_title=\"Date\",yaxis_title=\"Confirmed Cases\",legend=dict(x=0,y=1,traceorder=\"normal\"))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MA Model (using AUTO ARIMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ARIMA(0,2,0)(0,0,0)[0] intercept   : AIC=5440.501, Time=0.01 sec\n",
      " ARIMA(0,2,1)(0,0,0)[0] intercept   : AIC=5404.780, Time=0.03 sec\n",
      " ARIMA(0,2,2)(0,0,0)[0] intercept   : AIC=5397.146, Time=0.07 sec\n",
      "Total fit time: 0.124 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ARIMA(order=(0, 2, 2), scoring_args={}, suppress_warnings=True)"
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ma= auto_arima(model_train[\"cumulative_confirmed\"],trace=True, error_action='ignore', start_p=0,start_q=0,max_p=0,max_q=2,\n",
    "                   suppress_warnings=True,stepwise=False,seasonal=False)\n",
    "model_ma.fit(model_train[\"cumulative_confirmed\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_ma=model_ma.predict(len(test_set))\n",
    "y_pred[\"MA Model Prediction\"]=prediction_ma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error for MA Model:  402663.7053215682\n"
     ]
    }
   ],
   "source": [
    "model_scores.append(np.sqrt(mean_squared_error(y_pred[\"cumulative_confirmed\"][:valid.shape[0]-1],prediction_ma[:valid.shape[0]-1])))\n",
    "print(\"Root Mean Square Error for MA Model: \",np.sqrt(mean_squared_error(y_pred[\"cumulative_confirmed\"][:valid.shape[0]-1],prediction_ma[:valid.shape[0]-1])))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 534,
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   "source": [
    "fig=go.Figure()\n",
    "fig.add_trace(go.Scatter(x=model_train.index, y=model_train[\"cumulative_confirmed\"],\n",
    "                    mode='lines+markers',name=\"Train Data for Confirmed Cases\"))\n",
    "fig.add_trace(go.Scatter(x=valid.index, y=valid[\"cumulative_confirmed\"],\n",
    "                    mode='lines+markers',name=\"Validation Data for Confirmed Cases\",))\n",
    "fig.add_trace(go.Scatter(x=y_pred.index, y=y_pred[\"MA Model Prediction\"],\n",
    "                    mode='lines+markers',name=\"Prediction of Confirmed Cases\",))\n",
    "fig.update_layout(title=\"Confirmed Cases MA Model Prediction\",\n",
    "                 xaxis_title=\"Date\",yaxis_title=\"Confirmed Cases\",legend=dict(x=0,y=1,traceorder=\"normal\"))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ARIMA Model (using AUTOARIMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ARIMA(0,2,0)(0,0,0)[0] intercept   : AIC=5440.501, Time=0.02 sec\n",
      " ARIMA(0,2,1)(0,0,0)[0] intercept   : AIC=5404.780, Time=0.04 sec\n",
      " ARIMA(0,2,2)(0,0,0)[0] intercept   : AIC=5397.146, Time=0.07 sec\n",
      " ARIMA(0,2,3)(0,0,0)[0] intercept   : AIC=5400.984, Time=0.15 sec\n",
      " ARIMA(1,2,0)(0,0,0)[0] intercept   : AIC=5425.069, Time=0.04 sec\n",
      " ARIMA(1,2,1)(0,0,0)[0] intercept   : AIC=5397.558, Time=0.11 sec\n",
      " ARIMA(1,2,2)(0,0,0)[0] intercept   : AIC=5400.117, Time=0.12 sec\n",
      " ARIMA(1,2,3)(0,0,0)[0] intercept   : AIC=5375.323, Time=0.83 sec\n",
      " ARIMA(2,2,0)(0,0,0)[0] intercept   : AIC=5416.252, Time=0.06 sec\n",
      " ARIMA(2,2,1)(0,0,0)[0] intercept   : AIC=5394.256, Time=0.17 sec\n",
      " ARIMA(2,2,2)(0,0,0)[0] intercept   : AIC=5396.673, Time=0.65 sec\n",
      " ARIMA(2,2,3)(0,0,0)[0] intercept   : AIC=5348.817, Time=0.80 sec\n",
      " ARIMA(3,2,0)(0,0,0)[0] intercept   : AIC=5403.096, Time=0.10 sec\n",
      " ARIMA(3,2,1)(0,0,0)[0] intercept   : AIC=5387.673, Time=0.21 sec\n",
      " ARIMA(3,2,2)(0,0,0)[0] intercept   : AIC=5393.058, Time=0.36 sec\n",
      "Total fit time: 3.745 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ARIMA(order=(2, 2, 3), scoring_args={}, suppress_warnings=True)"
      ]
     },
     "execution_count": 535,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_arima= auto_arima(model_train[\"cumulative_confirmed\"],trace=True, error_action='ignore', start_p=1,start_q=1,max_p=3,max_q=3,\n",
    "                   suppress_warnings=True,stepwise=False,seasonal=False)\n",
    "model_arima.fit(model_train[\"cumulative_confirmed\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_arima=model_arima.predict(len(test_set))\n",
    "y_pred[\"ARIMA Model Prediction\"]=prediction_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error for ARIMA Model:  318989.4997088486\n"
     ]
    }
   ],
   "source": [
    "model_scores.append(np.sqrt(mean_squared_error(y_pred[\"cumulative_confirmed\"][:valid.shape[0]-1],prediction_arima[:valid.shape[0]-1])))\n",
    "print(\"Root Mean Square Error for ARIMA Model: \",np.sqrt(mean_squared_error(y_pred[\"cumulative_confirmed\"][:valid.shape[0]-1],prediction_arima[:valid.shape[0]-1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
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   "source": [
    "fig=go.Figure()\n",
    "fig.add_trace(go.Scatter(x=model_train.index, y=model_train[\"cumulative_confirmed\"],\n",
    "                    mode='lines+markers',name=\"Train Data for Confirmed Cases\"))\n",
    "fig.add_trace(go.Scatter(x=valid.index, y=valid[\"cumulative_confirmed\"],\n",
    "                    mode='lines+markers',name=\"Validation Data for Confirmed Cases\",))\n",
    "fig.add_trace(go.Scatter(x=y_pred.index, y=y_pred[\"ARIMA Model Prediction\"],\n",
    "                    mode='lines+markers',name=\"Prediction of Confirmed Cases\",))\n",
    "fig.update_layout(title=\"Confirmed Cases ARIMA Model Prediction\",\n",
    "                 xaxis_title=\"Date\",yaxis_title=\"Confirmed Cases\",legend=dict(x=0,y=1,traceorder=\"normal\"))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SARIMA Model (using AUTO ARIMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ARIMA(0,2,0)(0,0,0)[0] intercept   : AIC=5440.501, Time=0.02 sec\n",
      " ARIMA(0,2,1)(0,0,0)[0] intercept   : AIC=5404.780, Time=0.04 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/pmdarima/arima/_validation.py:60: UserWarning:\n",
      "\n",
      "m (7) set for non-seasonal fit. Setting to 0\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ARIMA(0,2,2)(0,0,0)[0] intercept   : AIC=5397.146, Time=0.08 sec\n",
      " ARIMA(1,2,0)(0,0,0)[0] intercept   : AIC=5425.069, Time=0.04 sec\n",
      " ARIMA(1,2,1)(0,0,0)[0] intercept   : AIC=5397.558, Time=0.11 sec\n",
      " ARIMA(1,2,2)(0,0,0)[0] intercept   : AIC=5400.117, Time=0.12 sec\n",
      " ARIMA(2,2,0)(0,0,0)[0] intercept   : AIC=5416.252, Time=0.04 sec\n",
      " ARIMA(2,2,1)(0,0,0)[0] intercept   : AIC=5394.256, Time=0.16 sec\n",
      " ARIMA(2,2,2)(0,0,0)[0] intercept   : AIC=5396.673, Time=0.63 sec\n",
      "Total fit time: 1.247 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ARIMA(order=(2, 2, 1), scoring_args={}, suppress_warnings=True)"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_sarima= auto_arima(model_train[\"cumulative_confirmed\"],trace=True, error_action='ignore', start_p=0,start_q=0,max_p=2,max_q=2,m=7,\n",
    "                   suppress_warnings=True,stepwise=False,seasonal=False)\n",
    "model_sarima.fit(model_train[\"cumulative_confirmed\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_sarima=model_sarima.predict(len(test_set))\n",
    "y_pred[\"MA Model Prediction\"]=prediction_ma.astype(int)\n",
    "y_pred[\"AR Model Prediction\"]=prediction_ar.astype(int)\n",
    "y_pred[\"ARIMA Model Prediction\"]=prediction_arima.astype(int)\n",
    "y_pred[\"SARIMA Model Prediction\"]=prediction_sarima.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error for SARIMA Model:  429544.20597818954\n"
     ]
    }
   ],
   "source": [
    "model_scores.append(np.sqrt(mean_squared_error(y_pred[\"cumulative_confirmed\"][:valid.shape[0]-1],y_pred[\"SARIMA Model Prediction\"][:valid.shape[0]-1])))\n",
    "print(\"Root Mean Square Error for SARIMA Model: \",np.sqrt(mean_squared_error(y_pred[\"cumulative_confirmed\"][:valid.shape[0]-1],y_pred[\"SARIMA Model Prediction\"][:valid.shape[0]-1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
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\"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Confirmed Cases SARIMA Model Prediction\"}, \"xaxis\": {\"title\": {\"text\": \"Date\"}}, \"yaxis\": {\"title\": {\"text\": \"Confirmed Cases\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('3ed0c251-3ab0-4fdc-bdbb-cd3978d73ebb');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=go.Figure()\n",
    "fig.add_trace(go.Scatter(x=model_train.index, y=model_train[\"cumulative_confirmed\"],\n",
    "                    mode='lines+markers',name=\"Train Data for Confirmed Cases\"))\n",
    "fig.add_trace(go.Scatter(x=valid.index, y=valid[\"cumulative_confirmed\"],\n",
    "                    mode='lines+markers',name=\"Validation Data for Confirmed Cases\",))\n",
    "fig.add_trace(go.Scatter(x=y_pred.index, y=y_pred[\"SARIMA Model Prediction\"],\n",
    "                    mode='lines+markers',name=\"Prediction for Confirmed Cases\",))\n",
    "fig.update_layout(title=\"Confirmed Cases SARIMA Model Prediction\",\n",
    "                 xaxis_title=\"Date\",yaxis_title=\"Confirmed Cases\",legend=dict(x=0,y=1,traceorder=\"normal\"))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Facebook's Prophet Model for forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_US_sim_FB = df_US_sim_ARM.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [],
   "source": [
    "prophet_c=Prophet(interval_width=0.95,weekly_seasonality=True,)\n",
    "prophet_confirmed=pd.DataFrame(zip(list(df_US_sim_FB.index),list(df_US_sim_FB[\"cumulative_confirmed\"])),columns=['ds','y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7f4f4cd75f90>"
      ]
     },
     "execution_count": 545,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prophet_c.fit(prophet_confirmed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_c=prophet_c.make_future_dataframe(periods=len(test_set))\n",
    "forecast_confirmed=forecast_c.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed_forecast=prophet_c.predict(forecast_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error for Prophet Model:  112945.9842093897\n"
     ]
    }
   ],
   "source": [
    "model_scores.append(np.sqrt(mean_squared_error(df_US_sim_FB[\"cumulative_confirmed\"],confirmed_forecast['yhat'].head(df_US_sim_FB.shape[0]))))\n",
    "print(\"Root Mean Squared Error for Prophet Model: \",np.sqrt(mean_squared_error(df_US_sim_FB[\"cumulative_confirmed\"],confirmed_forecast['yhat'].head(df_US_sim_FB.shape[0]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(720x432)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(prophet_c.plot(confirmed_forecast))\n",
    "y_pred[\"Facebook's Prophet\"]=confirmed_forecast['yhat'][-121:].to_numpy().astype(int)\n",
    "# confirmed_forecast['yhat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_pred = y_pred[['cumulative_confirmed','AR Model Prediction','MA Model Prediction','ARIMA Model Prediction','SARIMA Model Prediction']].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\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>Model Name</th>\n",
       "      <th>Root Mean Squared Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Facebook's Prophet Model</td>\n",
       "      <td>112945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ARIMA Model</td>\n",
       "      <td>318989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Moving Average Model (MA)</td>\n",
       "      <td>402663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SARIMA Model</td>\n",
       "      <td>429544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Holt's Winter Model</td>\n",
       "      <td>461543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Auto Regressive Model (AR)</td>\n",
       "      <td>469409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Holt's Linear</td>\n",
       "      <td>510568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>4460852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>4460852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest Regressor</td>\n",
       "      <td>4460852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Model Name  Root Mean Squared Error\n",
       "9    Facebook's Prophet Model                   112945\n",
       "7                 ARIMA Model                   318989\n",
       "6   Moving Average Model (MA)                   402663\n",
       "8                SARIMA Model                   429544\n",
       "4         Holt's Winter Model                   461543\n",
       "5  Auto Regressive Model (AR)                   469409\n",
       "3               Holt's Linear                   510568\n",
       "0                     XGBoost                  4460852\n",
       "1           Linear Regression                  4460852\n",
       "2     Random Forest Regressor                  4460852"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_names=[\"XGBoost\",\"Linear Regression\",\"Random Forest Regressor\",\"Holt's Linear\",\"Holt's Winter Model\",\n",
    "            \"Auto Regressive Model (AR)\",\"Moving Average Model (MA)\",\"ARIMA Model\",\"SARIMA Model\",\"Facebook's Prophet Model\"]\n",
    "model_summary=pd.DataFrame(zip(model_names,model_scores),columns=[\"Model Name\",\"Root Mean Squared Error\"]).sort_values([\"Root Mean Squared Error\"])\n",
    "model_summary['Root Mean Squared Error'] = model_summary['Root Mean Squared Error'].astype(int)\n",
    "model_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cumulative_confirmed</th>\n",
       "      <th>Holt</th>\n",
       "      <th>Holt's Winter Model</th>\n",
       "      <th>AR Model Prediction</th>\n",
       "      <th>MA Model Prediction</th>\n",
       "      <th>ARIMA Model Prediction</th>\n",
       "      <th>SARIMA Model Prediction</th>\n",
       "      <th>Facebook's Prophet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-14</th>\n",
       "      <td>7916100.0</td>\n",
       "      <td>7906176</td>\n",
       "      <td>7908379</td>\n",
       "      <td>7909837</td>\n",
       "      <td>7908594</td>\n",
       "      <td>7912611</td>\n",
       "      <td>7909075</td>\n",
       "      <td>10021215</td>\n",
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       "      <th>2020-10-15</th>\n",
       "      <td>7979885.0</td>\n",
       "      <td>7956504</td>\n",
       "      <td>7968196</td>\n",
       "      <td>7962044</td>\n",
       "      <td>7959906</td>\n",
       "      <td>7971368</td>\n",
       "      <td>7960069</td>\n",
       "      <td>10078457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-16</th>\n",
       "      <td>8050141.0</td>\n",
       "      <td>8006831</td>\n",
       "      <td>8033561</td>\n",
       "      <td>8011944</td>\n",
       "      <td>8011707</td>\n",
       "      <td>8029147</td>\n",
       "      <td>8011113</td>\n",
       "      <td>10136333</td>\n",
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       "    <tr>\n",
       "      <th>2020-10-17</th>\n",
       "      <td>8106752.0</td>\n",
       "      <td>8057159</td>\n",
       "      <td>8088992</td>\n",
       "      <td>8061474</td>\n",
       "      <td>8063997</td>\n",
       "      <td>8083471</td>\n",
       "      <td>8062728</td>\n",
       "      <td>10197166</td>\n",
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       "    <tr>\n",
       "      <th>2020-10-18</th>\n",
       "      <td>8154595.0</td>\n",
       "      <td>8107486</td>\n",
       "      <td>8131150</td>\n",
       "      <td>8112448</td>\n",
       "      <td>8116776</td>\n",
       "      <td>8134976</td>\n",
       "      <td>8114812</td>\n",
       "      <td>10258951</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>2021-02-07</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13744163</td>\n",
       "      <td>14057265</td>\n",
       "      <td>15037258</td>\n",
       "      <td>17123433</td>\n",
       "      <td>18934783</td>\n",
       "      <td>16321524</td>\n",
       "      <td>16914971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13794491</td>\n",
       "      <td>14101790</td>\n",
       "      <td>15109788</td>\n",
       "      <td>17231487</td>\n",
       "      <td>19071371</td>\n",
       "      <td>16416012</td>\n",
       "      <td>16979636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13844818</td>\n",
       "      <td>14152654</td>\n",
       "      <td>15182508</td>\n",
       "      <td>17340030</td>\n",
       "      <td>19208506</td>\n",
       "      <td>16510876</td>\n",
       "      <td>17048696</td>\n",
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       "    <tr>\n",
       "      <th>2021-02-10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13895146</td>\n",
       "      <td>14204876</td>\n",
       "      <td>15255418</td>\n",
       "      <td>17449063</td>\n",
       "      <td>19346708</td>\n",
       "      <td>16606114</td>\n",
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       "    <tr>\n",
       "      <th>2021-02-11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13945473</td>\n",
       "      <td>14264694</td>\n",
       "      <td>15328517</td>\n",
       "      <td>17558584</td>\n",
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       "      <td>16701729</td>\n",
       "      <td>17150479</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cumulative_confirmed      Holt  Holt's Winter Model  \\\n",
       "date                                                              \n",
       "2020-10-14             7916100.0   7906176              7908379   \n",
       "2020-10-15             7979885.0   7956504              7968196   \n",
       "2020-10-16             8050141.0   8006831              8033561   \n",
       "2020-10-17             8106752.0   8057159              8088992   \n",
       "2020-10-18             8154595.0   8107486              8131150   \n",
       "...                          ...       ...                  ...   \n",
       "2021-02-07                   0.0  13744163             14057265   \n",
       "2021-02-08                   0.0  13794491             14101790   \n",
       "2021-02-09                   0.0  13844818             14152654   \n",
       "2021-02-10                   0.0  13895146             14204876   \n",
       "2021-02-11                   0.0  13945473             14264694   \n",
       "\n",
       "            AR Model Prediction  MA Model Prediction  ARIMA Model Prediction  \\\n",
       "date                                                                           \n",
       "2020-10-14              7909837              7908594                 7912611   \n",
       "2020-10-15              7962044              7959906                 7971368   \n",
       "2020-10-16              8011944              8011707                 8029147   \n",
       "2020-10-17              8061474              8063997                 8083471   \n",
       "2020-10-18              8112448              8116776                 8134976   \n",
       "...                         ...                  ...                     ...   \n",
       "2021-02-07             15037258             17123433                18934783   \n",
       "2021-02-08             15109788             17231487                19071371   \n",
       "2021-02-09             15182508             17340030                19208506   \n",
       "2021-02-10             15255418             17449063                19346708   \n",
       "2021-02-11             15328517             17558584                19486228   \n",
       "\n",
       "            SARIMA Model Prediction  Facebook's Prophet  \n",
       "date                                                     \n",
       "2020-10-14                  7909075            10021215  \n",
       "2020-10-15                  7960069            10078457  \n",
       "2020-10-16                  8011113            10136333  \n",
       "2020-10-17                  8062728            10197166  \n",
       "2020-10-18                  8114812            10258951  \n",
       "...                             ...                 ...  \n",
       "2021-02-07                 16321524            16914971  \n",
       "2021-02-08                 16416012            16979636  \n",
       "2021-02-09                 16510876            17048696  \n",
       "2021-02-10                 16606114            17093237  \n",
       "2021-02-11                 16701729            17150479  \n",
       "\n",
       "[121 rows x 8 columns]"
      ]
     },
     "execution_count": 552,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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