Time_Series_Forecasting_with_Parametric_Curve_Fitting.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Time_Series_Forecasting_with_Parametric_Curve_Fitting.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/deepacefic/Covid19-Dashboard/blob/master/Time_Series_Forecasting_with_Parametric_Curve_Fitting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z65HKwgEC5Wo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "e3c5a194-8d53-4ed5-e97d-fc694f7d969f"
      },
      "source": [
        "pip install tf-utils"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tf-utils\n",
            "  Downloading https://files.pythonhosted.org/packages/bb/bf/f6800f5d34c2563849a900abaa8e8fdb44e859f9a45e45c35afcfd77d742/tf_utils-1.0.4-py3-none-any.whl\n",
            "Installing collected packages: tf-utils\n",
            "Successfully installed tf-utils-1.0.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNNSadoy194M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from tf_utils import *\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HFwx3_dq2DNx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "8c4c29a1-5f39-4426-a863-fc6c5927c9e9"
      },
      "source": [
        "dtf = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\", sep=\",\")\n",
        "dtf.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>1/30/20</th>\n",
              "      <th>1/31/20</th>\n",
              "      <th>2/1/20</th>\n",
              "      <th>2/2/20</th>\n",
              "      <th>2/3/20</th>\n",
              "      <th>2/4/20</th>\n",
              "      <th>2/5/20</th>\n",
              "      <th>2/6/20</th>\n",
              "      <th>2/7/20</th>\n",
              "      <th>2/8/20</th>\n",
              "      <th>2/9/20</th>\n",
              "      <th>2/10/20</th>\n",
              "      <th>2/11/20</th>\n",
              "      <th>2/12/20</th>\n",
              "      <th>2/13/20</th>\n",
              "      <th>2/14/20</th>\n",
              "      <th>2/15/20</th>\n",
              "      <th>2/16/20</th>\n",
              "      <th>2/17/20</th>\n",
              "      <th>2/18/20</th>\n",
              "      <th>2/19/20</th>\n",
              "      <th>2/20/20</th>\n",
              "      <th>2/21/20</th>\n",
              "      <th>2/22/20</th>\n",
              "      <th>2/23/20</th>\n",
              "      <th>2/24/20</th>\n",
              "      <th>2/25/20</th>\n",
              "      <th>2/26/20</th>\n",
              "      <th>...</th>\n",
              "      <th>5/10/20</th>\n",
              "      <th>5/11/20</th>\n",
              "      <th>5/12/20</th>\n",
              "      <th>5/13/20</th>\n",
              "      <th>5/14/20</th>\n",
              "      <th>5/15/20</th>\n",
              "      <th>5/16/20</th>\n",
              "      <th>5/17/20</th>\n",
              "      <th>5/18/20</th>\n",
              "      <th>5/19/20</th>\n",
              "      <th>5/20/20</th>\n",
              "      <th>5/21/20</th>\n",
              "      <th>5/22/20</th>\n",
              "      <th>5/23/20</th>\n",
              "      <th>5/24/20</th>\n",
              "      <th>5/25/20</th>\n",
              "      <th>5/26/20</th>\n",
              "      <th>5/27/20</th>\n",
              "      <th>5/28/20</th>\n",
              "      <th>5/29/20</th>\n",
              "      <th>5/30/20</th>\n",
              "      <th>5/31/20</th>\n",
              "      <th>6/1/20</th>\n",
              "      <th>6/2/20</th>\n",
              "      <th>6/3/20</th>\n",
              "      <th>6/4/20</th>\n",
              "      <th>6/5/20</th>\n",
              "      <th>6/6/20</th>\n",
              "      <th>6/7/20</th>\n",
              "      <th>6/8/20</th>\n",
              "      <th>6/9/20</th>\n",
              "      <th>6/10/20</th>\n",
              "      <th>6/11/20</th>\n",
              "      <th>6/12/20</th>\n",
              "      <th>6/13/20</th>\n",
              "      <th>6/14/20</th>\n",
              "      <th>6/15/20</th>\n",
              "      <th>6/16/20</th>\n",
              "      <th>6/17/20</th>\n",
              "      <th>6/18/20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>33.0000</td>\n",
              "      <td>65.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>4402</td>\n",
              "      <td>4687</td>\n",
              "      <td>4963</td>\n",
              "      <td>5226</td>\n",
              "      <td>5639</td>\n",
              "      <td>6053</td>\n",
              "      <td>6402</td>\n",
              "      <td>6664</td>\n",
              "      <td>7072</td>\n",
              "      <td>7653</td>\n",
              "      <td>8145</td>\n",
              "      <td>8676</td>\n",
              "      <td>9216</td>\n",
              "      <td>9998</td>\n",
              "      <td>10582</td>\n",
              "      <td>11173</td>\n",
              "      <td>11831</td>\n",
              "      <td>12456</td>\n",
              "      <td>13036</td>\n",
              "      <td>13659</td>\n",
              "      <td>14525</td>\n",
              "      <td>15205</td>\n",
              "      <td>15750</td>\n",
              "      <td>16509</td>\n",
              "      <td>17267</td>\n",
              "      <td>18054</td>\n",
              "      <td>18969</td>\n",
              "      <td>19551</td>\n",
              "      <td>20342</td>\n",
              "      <td>20917</td>\n",
              "      <td>21459</td>\n",
              "      <td>22142</td>\n",
              "      <td>22890</td>\n",
              "      <td>23546</td>\n",
              "      <td>24102</td>\n",
              "      <td>24766</td>\n",
              "      <td>25527</td>\n",
              "      <td>26310</td>\n",
              "      <td>26874</td>\n",
              "      <td>27532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Albania</td>\n",
              "      <td>41.1533</td>\n",
              "      <td>20.1683</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>868</td>\n",
              "      <td>872</td>\n",
              "      <td>876</td>\n",
              "      <td>880</td>\n",
              "      <td>898</td>\n",
              "      <td>916</td>\n",
              "      <td>933</td>\n",
              "      <td>946</td>\n",
              "      <td>948</td>\n",
              "      <td>949</td>\n",
              "      <td>964</td>\n",
              "      <td>969</td>\n",
              "      <td>981</td>\n",
              "      <td>989</td>\n",
              "      <td>998</td>\n",
              "      <td>1004</td>\n",
              "      <td>1029</td>\n",
              "      <td>1050</td>\n",
              "      <td>1076</td>\n",
              "      <td>1099</td>\n",
              "      <td>1122</td>\n",
              "      <td>1137</td>\n",
              "      <td>1143</td>\n",
              "      <td>1164</td>\n",
              "      <td>1184</td>\n",
              "      <td>1197</td>\n",
              "      <td>1212</td>\n",
              "      <td>1232</td>\n",
              "      <td>1246</td>\n",
              "      <td>1263</td>\n",
              "      <td>1299</td>\n",
              "      <td>1341</td>\n",
              "      <td>1385</td>\n",
              "      <td>1416</td>\n",
              "      <td>1464</td>\n",
              "      <td>1521</td>\n",
              "      <td>1590</td>\n",
              "      <td>1672</td>\n",
              "      <td>1722</td>\n",
              "      <td>1788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>28.0339</td>\n",
              "      <td>1.6596</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>5723</td>\n",
              "      <td>5891</td>\n",
              "      <td>6067</td>\n",
              "      <td>6253</td>\n",
              "      <td>6442</td>\n",
              "      <td>6629</td>\n",
              "      <td>6821</td>\n",
              "      <td>7019</td>\n",
              "      <td>7201</td>\n",
              "      <td>7377</td>\n",
              "      <td>7542</td>\n",
              "      <td>7728</td>\n",
              "      <td>7918</td>\n",
              "      <td>8113</td>\n",
              "      <td>8306</td>\n",
              "      <td>8503</td>\n",
              "      <td>8697</td>\n",
              "      <td>8857</td>\n",
              "      <td>8997</td>\n",
              "      <td>9134</td>\n",
              "      <td>9267</td>\n",
              "      <td>9394</td>\n",
              "      <td>9513</td>\n",
              "      <td>9626</td>\n",
              "      <td>9733</td>\n",
              "      <td>9831</td>\n",
              "      <td>9935</td>\n",
              "      <td>10050</td>\n",
              "      <td>10154</td>\n",
              "      <td>10265</td>\n",
              "      <td>10382</td>\n",
              "      <td>10484</td>\n",
              "      <td>10589</td>\n",
              "      <td>10698</td>\n",
              "      <td>10810</td>\n",
              "      <td>10919</td>\n",
              "      <td>11031</td>\n",
              "      <td>11147</td>\n",
              "      <td>11268</td>\n",
              "      <td>11385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Andorra</td>\n",
              "      <td>42.5063</td>\n",
              "      <td>1.5218</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>755</td>\n",
              "      <td>755</td>\n",
              "      <td>758</td>\n",
              "      <td>760</td>\n",
              "      <td>761</td>\n",
              "      <td>761</td>\n",
              "      <td>761</td>\n",
              "      <td>761</td>\n",
              "      <td>761</td>\n",
              "      <td>761</td>\n",
              "      <td>762</td>\n",
              "      <td>762</td>\n",
              "      <td>762</td>\n",
              "      <td>762</td>\n",
              "      <td>762</td>\n",
              "      <td>763</td>\n",
              "      <td>763</td>\n",
              "      <td>763</td>\n",
              "      <td>763</td>\n",
              "      <td>764</td>\n",
              "      <td>764</td>\n",
              "      <td>764</td>\n",
              "      <td>765</td>\n",
              "      <td>844</td>\n",
              "      <td>851</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>853</td>\n",
              "      <td>853</td>\n",
              "      <td>853</td>\n",
              "      <td>853</td>\n",
              "      <td>854</td>\n",
              "      <td>854</td>\n",
              "      <td>855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>-11.2027</td>\n",
              "      <td>17.8739</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>50</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>58</td>\n",
              "      <td>60</td>\n",
              "      <td>61</td>\n",
              "      <td>69</td>\n",
              "      <td>70</td>\n",
              "      <td>70</td>\n",
              "      <td>71</td>\n",
              "      <td>74</td>\n",
              "      <td>81</td>\n",
              "      <td>84</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>88</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>96</td>\n",
              "      <td>113</td>\n",
              "      <td>118</td>\n",
              "      <td>130</td>\n",
              "      <td>138</td>\n",
              "      <td>140</td>\n",
              "      <td>142</td>\n",
              "      <td>148</td>\n",
              "      <td>155</td>\n",
              "      <td>166</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 153 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region      Lat  ...  6/16/20  6/17/20  6/18/20\n",
              "0            NaN    Afghanistan  33.0000  ...    26310    26874    27532\n",
              "1            NaN        Albania  41.1533  ...     1672     1722     1788\n",
              "2            NaN        Algeria  28.0339  ...    11147    11268    11385\n",
              "3            NaN        Andorra  42.5063  ...      854      854      855\n",
              "4            NaN         Angola -11.2027  ...      148      155      166\n",
              "\n",
              "[5 rows x 153 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZp1oa6zvSMv",
        "colab_type": "text"
      },
      "source": [
        "# Curve fitting for **India**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4IqAmx53z7l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "## groupby country\n",
        "dtf = dtf.drop(['Province/State','Lat','Long'], axis=1).groupby(\"Country/Region\").sum().T\n",
        "\n",
        "## convert index to datetime\n",
        "dtf.index = pd.to_datetime(dtf.index, infer_datetime_format=True)\n",
        "\n",
        "## create total cases column\n",
        "dtf = pd.DataFrame(index=dtf.index, data=dtf[\"India\"].values, columns=[\"total\"])\n",
        "\n",
        "## create daily changes column\n",
        "dtf[\"new\"] = dtf[\"total\"] - dtf[\"total\"].shift(1)\n",
        "dtf[\"new\"] = dtf[\"new\"].fillna(method='bfill')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0XyPSDTJlT5c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "f7b86dbf-0f10-46a4-cece-52a979ca1b5b"
      },
      "source": [
        "dtf.tail(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>total</th>\n",
              "      <th>new</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-06-09</th>\n",
              "      <td>276146</td>\n",
              "      <td>10218.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-10</th>\n",
              "      <td>286605</td>\n",
              "      <td>10459.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-11</th>\n",
              "      <td>297535</td>\n",
              "      <td>10930.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-12</th>\n",
              "      <td>308993</td>\n",
              "      <td>11458.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-13</th>\n",
              "      <td>320922</td>\n",
              "      <td>11929.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-14</th>\n",
              "      <td>332424</td>\n",
              "      <td>11502.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-15</th>\n",
              "      <td>343091</td>\n",
              "      <td>10667.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-16</th>\n",
              "      <td>354065</td>\n",
              "      <td>10974.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-17</th>\n",
              "      <td>366946</td>\n",
              "      <td>12881.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-18</th>\n",
              "      <td>380532</td>\n",
              "      <td>13586.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             total      new\n",
              "2020-06-09  276146  10218.0\n",
              "2020-06-10  286605  10459.0\n",
              "2020-06-11  297535  10930.0\n",
              "2020-06-12  308993  11458.0\n",
              "2020-06-13  320922  11929.0\n",
              "2020-06-14  332424  11502.0\n",
              "2020-06-15  343091  10667.0\n",
              "2020-06-16  354065  10974.0\n",
              "2020-06-17  366946  12881.0\n",
              "2020-06-18  380532  13586.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tPK2I0MJPpB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "f264cf0b-216f-492c-b760-2c8ff4a12fd5"
      },
      "source": [
        "fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(13,7))\n",
        "ax[0].scatter(dtf.index, dtf[\"total\"].values, color=\"black\")\n",
        "ax[0].set(title=\"total cases\")\n",
        "ax[1].bar(dtf.index, dtf[\"new\"].values, color=\"black\")\n",
        "ax[1].set(title=\"new cases\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 936x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXztrXr1Rru3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "'''\n",
        "Linear function: f(x) = a + b*x\n",
        "'''\n",
        "def f(x):\n",
        "    return 10 + 2500*x\n",
        "\n",
        "y_linear = f(x=np.arange(len(dtf)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3p2ifJcR80P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'''\n",
        "Exponential function: f(x) = a + b^x\n",
        "'''\n",
        "def f(x):\n",
        "    return 10 + 1.091**x\n",
        "\n",
        "y_exponential = f(x=np.arange(len(dtf)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6QSTitOSCK-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'''\n",
        "Logistic function: f(x) = a / (1 + e^(-b*(x-c)))\n",
        "'''\n",
        "def f(x): \n",
        "    return 350000 / (1 + np.exp(-0.5*(x-20)))\n",
        "\n",
        "y_logistic = f(x=np.arange(len(dtf)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4mjMVNiSIRw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "f1d64373-50f5-4ae6-bb5f-ebd39cf1f9f7"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(13,5))\n",
        "ax.scatter(dtf[\"total\"].index, dtf[\"total\"].values, color=\"black\")\n",
        "ax.plot(dtf[\"total\"].index, y_linear, label=\"linear\", color=\"red\")\n",
        "ax.plot(dtf[\"total\"].index, y_exponential, label=\"exponential\", color=\"green\")\n",
        "ax.plot(dtf[\"total\"].index, y_logistic, label=\"logistic\", color=\"blue\")\n",
        "ax.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 936x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDaHF1DIWzbh",
        "colab_type": "text"
      },
      "source": [
        "#-> Data starts low and grows exponentially ... looks similar to a exponential function. But I know for fact that\n",
        "#   this phenomenon has an upper limit, because the virus can't infect more than the total population of the country.\n",
        "#   Therefore the logistic function is more appropriate to model this data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9LD61uxWuxb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'''\n",
        "Gaussian function: f(x) = a * e^(-0.5 * ((x-μ)/σ)**2)\n",
        "'''\n",
        "def f(x):\n",
        "    return 12000 * np.exp(-0.5 * ((x-145)/21)**2)\n",
        "\n",
        "y_gaussian = f(x=np.arange(len(dtf)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1OsFmYqmSqM1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "de033ede-0b44-484a-d179-7abfff33dd27"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(13,5))\n",
        "ax.bar(dtf[\"new\"].index, dtf[\"new\"].values, color=\"black\")\n",
        "ax.plot(dtf[\"new\"].index, y_gaussian, color=\"red\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 936x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NvAwEcGJaAbX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0a721ff9-cb4d-4e05-8a6f-2d0135c6807d"
      },
      "source": [
        "from scipy.optimize import curve_fit\n",
        "'''\n",
        "Logistic function: f(x) = capacity / (1 + e^-k*(x - midpoint) )\n",
        "'''\n",
        "def logistic_f(X, c, k, m):\n",
        "    y = c / (1 + np.exp(-k*(X-m)))\n",
        "    return y\n",
        "## optimize from scipy\n",
        "X=np.arange(len(dtf[\"total\"]))\n",
        "y=dtf[\"total\"].values\n",
        "logistic_model, cov = curve_fit(logistic_f,\n",
        "                                X, \n",
        "                               y , \n",
        "                                maxfev=10000,\n",
        "                                p0=[np.max(dtf[\"total\"]), 1, 1])\n",
        "## print the parameters\n",
        "logistic_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6.88990280e+05, 6.31420470e-02, 1.45149004e+02])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4k2L3kkiwLT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ee812d93-a2de-4a20-d020-5ff6611ae5ef"
      },
      "source": [
        "'''\n",
        "Gaussian function: f(x) = a * e^(-0.5 * ((x-μ)/σ)**2)\n",
        "'''\n",
        "def gaussian_f(X, a, b, c):\n",
        "    y_prime = a * np.exp(-0.5 * ((X-b)/c)**2)\n",
        "    return y_prime\n",
        "## optimize from scipy\n",
        "X1=np.arange(len(dtf[\"new\"]))\n",
        "y1=dtf[\"new\"].values\n",
        "gaussian_model, cov = curve_fit(gaussian_f,\n",
        "                                X1, \n",
        "                                y1, \n",
        "                                maxfev=10000,\n",
        "                                p0=[1,np.mean(dtf[\"new\"]),1])\n",
        "## print the parameters\n",
        "gaussian_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.00000000e+00, 2.55390604e+03, 1.00000000e+00])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHXMpZHCjTGS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'''\n",
        "Plot parametric fitting fuction.\n",
        "'''\n",
        "def utils_plot_parametric(dtf, zoom=30, figsize=(15,5)):\n",
        "    ## interval\n",
        "    dtf[\"residuals\"] = dtf[\"ts\"] - dtf[\"model\"]\n",
        "    dtf[\"conf_int_low\"] = dtf[\"forecast\"] - 1.96*dtf[\"residuals\"].std()\n",
        "    dtf[\"conf_int_up\"] = dtf[\"forecast\"] + 1.96*dtf[\"residuals\"].std()\n",
        "    fig, ax = plt.subplots(nrows=1, ncols=2, figsize=figsize)\n",
        "    \n",
        "    ## entire series\n",
        "    dtf[\"ts\"].plot(marker=\".\", linestyle='None', ax=ax[0], title=\"Parametric Fitting\", color=\"black\")\n",
        "    dtf[\"model\"].plot(ax=ax[0], color=\"green\")\n",
        "    dtf[\"forecast\"].plot(ax=ax[0], grid=True, color=\"red\")\n",
        "    ax[0].fill_between(x=dtf.index, y1=dtf['conf_int_low'], y2=dtf['conf_int_up'], color='b', alpha=0.3)\n",
        "   \n",
        "    ## focus on last\n",
        "    first_idx = dtf[pd.notnull(dtf[\"forecast\"])].index[0]\n",
        "    first_loc = dtf.index.tolist().index(first_idx)\n",
        "    zoom_idx = dtf.index[first_loc-zoom]\n",
        "    dtf.loc[zoom_idx:][\"ts\"].plot(marker=\".\", linestyle='None', ax=ax[1], color=\"black\", \n",
        "                                  title=\"Zoom on the last \"+str(zoom)+\" observations\")\n",
        "    dtf.loc[zoom_idx:][\"model\"].plot(ax=ax[1], color=\"green\")\n",
        "    dtf.loc[zoom_idx:][\"forecast\"].plot(ax=ax[1], grid=True, color=\"red\")\n",
        "    ax[1].fill_between(x=dtf.loc[zoom_idx:].index, y1=dtf.loc[zoom_idx:]['conf_int_low'], \n",
        "                       y2=dtf.loc[zoom_idx:]['conf_int_up'], color='b', alpha=0.3)\n",
        "    plt.show()\n",
        "    \n",
        "    return dtf[[\"ts\",\"model\",\"residuals\",\"conf_int_low\",\"forecast\",\"conf_int_up\"]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7xGuHg_jVgZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'''\n",
        "Forecast unknown future.\n",
        ":parameter\n",
        "    :param ts: pandas series\n",
        "    :param f: function\n",
        "    :param model: list of optim params\n",
        "    :param pred_ahead: number of observations to forecast (ex. pred_ahead=30)\n",
        "    :param freq: None or str - 'B' business day, 'D' daily, 'W' weekly, 'M' monthly, 'A' annual, 'Q' quarterly\n",
        "    :param zoom: for plotting\n",
        "'''\n",
        "def forecast_curve(ts, f, model, pred_ahead=None, freq=\"D\", zoom=30, figsize=(15,5)):\n",
        "    ## fit\n",
        "    X = np.arange(len(ts))\n",
        "    fitted = f(X, model[0], model[1], model[2])\n",
        "    dtf = ts.to_frame(name=\"ts\")\n",
        "    dtf[\"model\"] = fitted\n",
        "    \n",
        "    ## index\n",
        "    index = pd.date_range(start='2020-06-17',periods=pred_ahead,freq=freq)\n",
        "    index = index[1:]\n",
        "    ## forecast\n",
        "    Xnew = np.arange(len(ts)+1, len(ts)+1+len(index))\n",
        "    preds = f(Xnew, model[0], model[1], model[2])\n",
        "    dtf = dtf.append(pd.DataFrame(data=preds, index=index, columns=[\"forecast\"]))\n",
        "    \n",
        "    ## plot\n",
        "    utils_plot_parametric(dtf, zoom=zoom)\n",
        "    return dtf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aVOLr8qvjcxl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "14353ab8-04a4-425e-b228-50bca18d77e4"
      },
      "source": [
        "preds = forecast_curve(dtf[\"total\"], logistic_f, logistic_model, \n",
        "                       pred_ahead=30, freq=\"D\", zoom=7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53wUhtE3tN0q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "fdcfbf04-88f5-4e71-94b0-f696317c1070"
      },
      "source": [
        "## Forecast\n",
        "preds = forecast_curve(dtf[\"new\"], gaussian_f, gaussian_model, pred_ahead=30,freq=\"D\", zoom=7, figsize=(15,5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8SPSbbxWJuf6",
        "colab_type": "text"
      },
      "source": [
        "For some reason we are not getting good fit with gaussian function, I guess it's just the matter of parameter scaling."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LsacqXcFJ6hC",
        "colab_type": "text"
      },
      "source": [
        "Trying with lmfit, if we can get the results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zogj3etxEwib",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "e3ef643e-3d60-42b7-b5c3-9c9349d6b57d"
      },
      "source": [
        "!pip install lmfit"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting lmfit\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d1/2e/9e4cdcebf93603ce57bc7577707ad56ed1282ae306f611e525612b9cfeea/lmfit-1.0.1.tar.gz (258kB)\n",
            "\u001b[K     |████████████████████████████████| 266kB 2.8MB/s \n",
            "\u001b[?25hCollecting asteval>=0.9.16\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e4/3b/8aaee90977588fa3e88d7a495af306d6f4a1c1c01c8f0fe0de1fc43d0908/asteval-0.9.18.tar.gz (52kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 6.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.6/dist-packages (from lmfit) (1.18.5)\n",
            "Requirement already satisfied: scipy>=1.2 in /usr/local/lib/python3.6/dist-packages (from lmfit) (1.4.1)\n",
            "Collecting uncertainties>=3.0.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b0/e0/fc200da8190729dcb685ae4877ed6936d31d64aeccb8cc355d9ec982681d/uncertainties-3.1.4-py2.py3-none-any.whl (246kB)\n",
            "\u001b[K     |████████████████████████████████| 256kB 9.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from uncertainties>=3.0.1->lmfit) (0.16.0)\n",
            "Building wheels for collected packages: lmfit, asteval\n",
            "  Building wheel for lmfit (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lmfit: filename=lmfit-1.0.1-cp36-none-any.whl size=81990 sha256=f093c207d1a114ced7dbfdc7bf1be959c27dcab0729ddc11cd1cb7110543ad4b\n",
            "  Stored in directory: /root/.cache/pip/wheels/ef/22/8d/6cd9c0af36f501cfb248673b05dad5d2c27504951d55519acb\n",
            "  Building wheel for asteval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for asteval: filename=asteval-0.9.18-cp36-none-any.whl size=17381 sha256=7fa5a95208fde9175a8be459e9f48eab4213fed7d3956554aa0beb89c3d9e956\n",
            "  Stored in directory: /root/.cache/pip/wheels/1c/53/1b/28d929700c9633b1786a4982e6db564ec8326a8930234dcc19\n",
            "Successfully built lmfit asteval\n",
            "Installing collected packages: asteval, uncertainties, lmfit\n",
            "Successfully installed asteval-0.9.18 lmfit-1.0.1 uncertainties-3.1.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlSj3S_vD8gu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "outputId": "99aa63b9-9fa0-459a-8c25-39591eaf4fdb"
      },
      "source": [
        "from lmfit import Model\n",
        "import math\n",
        "def gaussian(x, amp, cen, wid):\n",
        "    \"\"\"1-d gaussian: gaussian(x, amp, cen, wid)\"\"\"\n",
        "    return (amp / (math.sqrt(2*math.pi) * wid)) * np.exp(-(x-cen)**2 / (2*wid**2))\n",
        "\n",
        "\n",
        "gmodel = Model(gaussian)\n",
        "result = gmodel.fit(y1, x=X1, amp=1, cen=np.mean(dtf[\"new\"]), wid=1)\n",
        "\n",
        "print(result.fit_report())\n",
        "\n",
        "plt.plot(X1, y1, 'bo')\n",
        "plt.plot(X1, result.init_fit, 'k--', label='initial fit')\n",
        "plt.plot(X1, result.best_fit, 'r-', label='best fit')\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n",
        "# <end examples/doc_model_gaussian.py>"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[Model]]\n",
            "    Model(gaussian)\n",
            "[[Fit Statistics]]\n",
            "    # fitting method   = leastsq\n",
            "    # function evals   = 4\n",
            "    # data points      = 149\n",
            "    # variables        = 3\n",
            "    chi-square         = 2.9719e+09\n",
            "    reduced chi-square = 20355151.4\n",
            "    Akaike info crit   = 2510.46723\n",
            "    Bayesian info crit = 2519.47907\n",
            "##  Warning: uncertainties could not be estimated:\n",
            "    amp:  at initial value\n",
            "    cen:  at initial value\n",
            "    wid:  at initial value\n",
            "[[Variables]]\n",
            "    amp:  1.00000000 (init = 1)\n",
            "    cen:  2553.90604 (init = 2553.906)\n",
            "    wid:  1.00000000 (init = 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3NABTGQw4yv",
        "colab_type": "text"
      },
      "source": [
        "This happening because, data has not taken gaussian shape yet,so curve_fit as well lmfit may not be able to fit properly."
      ]
    }
  ]
}