Onion_price_forecasting_ARIMA.ipynb

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    "accelerator": "GPU",
    "colab": {
      "name": "Onion price forecasting.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM64/yczOoqnmFaibQ16Q+c",
      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hariPrasadCoder/Onion-price-forecasting-and-supply-chain-analytics/blob/main/Onion_price_forecasting_ARIMA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xHpkxZ15tc3h"
      },
      "source": [
        "# ONION PRICE FORECASTING USING ARIMA MODEL:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eUM9hTvp3HON"
      },
      "source": [
        "## Importing necessary basic libraries:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6shR-iaMxRAJ"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhdI25Qe3Oh2"
      },
      "source": [
        "## Reading the dataset:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "IBQiceo4x9HC",
        "outputId": "146aceee-199c-4375-edd6-4750c11a240f"
      },
      "source": [
        "df = pd.read_excel('MonthWiseMarketArrivals_Chennai.xlsx')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>market</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>quantity</th>\n",
              "      <th>priceMin</th>\n",
              "      <th>priceMax</th>\n",
              "      <th>priceMod</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHENNAI</td>\n",
              "      <td>January</td>\n",
              "      <td>2004</td>\n",
              "      <td>103400</td>\n",
              "      <td>798</td>\n",
              "      <td>1019</td>\n",
              "      <td>910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHENNAI</td>\n",
              "      <td>February</td>\n",
              "      <td>2004</td>\n",
              "      <td>87800</td>\n",
              "      <td>776</td>\n",
              "      <td>969</td>\n",
              "      <td>873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHENNAI</td>\n",
              "      <td>March</td>\n",
              "      <td>2004</td>\n",
              "      <td>102180</td>\n",
              "      <td>506</td>\n",
              "      <td>656</td>\n",
              "      <td>580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHENNAI</td>\n",
              "      <td>April</td>\n",
              "      <td>2004</td>\n",
              "      <td>83300</td>\n",
              "      <td>448</td>\n",
              "      <td>599</td>\n",
              "      <td>527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHENNAI</td>\n",
              "      <td>May</td>\n",
              "      <td>2004</td>\n",
              "      <td>84850</td>\n",
              "      <td>462</td>\n",
              "      <td>596</td>\n",
              "      <td>529</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    market     month  year  quantity  priceMin  priceMax  priceMod\n",
              "0  CHENNAI   January  2004    103400       798      1019       910\n",
              "1  CHENNAI  February  2004     87800       776       969       873\n",
              "2  CHENNAI     March  2004    102180       506       656       580\n",
              "3  CHENNAI     April  2004     83300       448       599       527\n",
              "4  CHENNAI       May  2004     84850       462       596       529"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "7pSqJpUEyKx-",
        "outputId": "87bdc078-230b-439f-88e3-7f2cf7a4f2a5"
      },
      "source": [
        "df = df[['month','year','priceMod']]\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>priceMod</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>January</td>\n",
              "      <td>2004</td>\n",
              "      <td>910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>February</td>\n",
              "      <td>2004</td>\n",
              "      <td>873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>March</td>\n",
              "      <td>2004</td>\n",
              "      <td>580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>April</td>\n",
              "      <td>2004</td>\n",
              "      <td>527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>May</td>\n",
              "      <td>2004</td>\n",
              "      <td>529</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      month  year  priceMod\n",
              "0   January  2004       910\n",
              "1  February  2004       873\n",
              "2     March  2004       580\n",
              "3     April  2004       527\n",
              "4       May  2004       529"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f3hhRra93TxK"
      },
      "source": [
        "## Combining \"month\" and \"year\" columns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "H-nVFAaDyftd",
        "outputId": "6195fd86-8c62-4488-e232-83efd78ba8c7"
      },
      "source": [
        "df['date'] = df['month'] + '-' + df['year'].map(str)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>priceMod</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>January</td>\n",
              "      <td>2004</td>\n",
              "      <td>910</td>\n",
              "      <td>January-2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>February</td>\n",
              "      <td>2004</td>\n",
              "      <td>873</td>\n",
              "      <td>February-2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>March</td>\n",
              "      <td>2004</td>\n",
              "      <td>580</td>\n",
              "      <td>March-2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>April</td>\n",
              "      <td>2004</td>\n",
              "      <td>527</td>\n",
              "      <td>April-2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>May</td>\n",
              "      <td>2004</td>\n",
              "      <td>529</td>\n",
              "      <td>May-2004</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      month  year  priceMod           date\n",
              "0   January  2004       910   January-2004\n",
              "1  February  2004       873  February-2004\n",
              "2     March  2004       580     March-2004\n",
              "3     April  2004       527     April-2004\n",
              "4       May  2004       529       May-2004"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JyoyiZszNoa"
      },
      "source": [
        "df.date = pd.to_datetime(df.date)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "s0RNRDxRzsKH",
        "outputId": "b717d9ff-4445-4707-eebb-9f0f348a9978"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>priceMod</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>January</td>\n",
              "      <td>2004</td>\n",
              "      <td>910</td>\n",
              "      <td>2004-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>February</td>\n",
              "      <td>2004</td>\n",
              "      <td>873</td>\n",
              "      <td>2004-02-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>March</td>\n",
              "      <td>2004</td>\n",
              "      <td>580</td>\n",
              "      <td>2004-03-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>April</td>\n",
              "      <td>2004</td>\n",
              "      <td>527</td>\n",
              "      <td>2004-04-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>May</td>\n",
              "      <td>2004</td>\n",
              "      <td>529</td>\n",
              "      <td>2004-05-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      month  year  priceMod       date\n",
              "0   January  2004       910 2004-01-01\n",
              "1  February  2004       873 2004-02-01\n",
              "2     March  2004       580 2004-03-01\n",
              "3     April  2004       527 2004-04-01\n",
              "4       May  2004       529 2004-05-01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pX_HnOC5tnL6"
      },
      "source": [
        "## Considering only the \"date\" and \"Price\" features for further steps:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "lgXY06n3z1wC",
        "outputId": "47775726-58a6-4147-b118-f96f0d14c0f8"
      },
      "source": [
        "df = df[['date','priceMod']]\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>priceMod</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2004-01-01</td>\n",
              "      <td>910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2004-02-01</td>\n",
              "      <td>873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2004-03-01</td>\n",
              "      <td>580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2004-04-01</td>\n",
              "      <td>527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2004-05-01</td>\n",
              "      <td>529</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  priceMod\n",
              "0 2004-01-01       910\n",
              "1 2004-02-01       873\n",
              "2 2004-03-01       580\n",
              "3 2004-04-01       527\n",
              "4 2004-05-01       529"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOAoB8-tt2E4"
      },
      "source": [
        "## Setting the \"date\" column as an index:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yI4t-VeH1HPX"
      },
      "source": [
        "df = df.set_index('date')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "vSMqiRmi1J5N",
        "outputId": "bc2592ea-4bc0-4074-e252-cabf4509da36"
      },
      "source": [
        "df.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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>priceMod</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2004-01-01</th>\n",
              "      <td>910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-02-01</th>\n",
              "      <td>873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-03-01</th>\n",
              "      <td>580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-04-01</th>\n",
              "      <td>527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-05-01</th>\n",
              "      <td>529</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            priceMod\n",
              "date                \n",
              "2004-01-01       910\n",
              "2004-02-01       873\n",
              "2004-03-01       580\n",
              "2004-04-01       527\n",
              "2004-05-01       529"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cm5lPWhtt8cK"
      },
      "source": [
        "## Converting DataFrame to Series:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJka0AGO1LdQ",
        "outputId": "0167afb9-0336-48db-9743-2ee25fb66d63"
      },
      "source": [
        "df = df.squeeze()\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date\n",
              "2004-01-01     910\n",
              "2004-02-01     873\n",
              "2004-03-01     580\n",
              "2004-04-01     527\n",
              "2004-05-01     529\n",
              "              ... \n",
              "2021-01-01    2976\n",
              "2021-02-01    4211\n",
              "2021-03-01    3300\n",
              "2021-04-01    1100\n",
              "2021-05-01    1150\n",
              "Name: priceMod, Length: 209, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3L9fcN8buAnj"
      },
      "source": [
        "## Splitting the dataset to train and validation, and saving it as separate csv files:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F29yb2dS18Zr",
        "outputId": "830b7ab8-f23e-486b-8c06-b3023ae75d13"
      },
      "source": [
        "split_point = len(df) - 5\n",
        "dataset, validation = df[0:split_point], df[split_point:]\n",
        "print('Dataset %d, Validation %d' % (len(dataset), len(validation)))\n",
        "dataset.to_csv('onion_dataset_final.csv', header=False)\n",
        "validation.to_csv('onion_validation.csv', header=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset 204, Validation 5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "48rS7HQCuPCD"
      },
      "source": [
        "## Fitting the model using a baseline model called \"Persistence model\":"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6WcDP7tuO7R"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QtPPh34i3h0M",
        "outputId": "2b9dfaf0-2ba4-47b6-8d54-0505f38f39a9"
      },
      "source": [
        "# evaluate a persistence model\n",
        "series = pd.read_csv('onion_dataset_final.csv', header=None, index_col=0, parse_dates=True, squeeze=True)\n",
        "# prepare data\n",
        "X = series.values\n",
        "X = X.astype('float32')\n",
        "train_size = int(len(X) * 0.50)\n",
        "train, test = X[0:train_size], X[train_size:]\n",
        "# walk-forward validation\n",
        "history = [x for x in train]\n",
        "predictions = list()\n",
        "for i in range(len(test)):\n",
        "  # predict\n",
        "  yhat = history[-1]\n",
        "  predictions.append(yhat)\n",
        "  # observation\n",
        "  obs = test[i]\n",
        "  history.append(obs)\n",
        "  print('>Predicted=%.3f, Expected=%.3f' % (yhat, obs))\n",
        "# report performance\n",
        "rmse = sqrt(mean_squared_error(test, predictions))\n",
        "print('RMSE: %.3f' % rmse)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ">Predicted=887.000, Expected=1038.000\n",
            ">Predicted=1038.000, Expected=1143.000\n",
            ">Predicted=1143.000, Expected=1123.000\n",
            ">Predicted=1123.000, Expected=1269.000\n",
            ">Predicted=1269.000, Expected=1921.000\n",
            ">Predicted=1921.000, Expected=1910.000\n",
            ">Predicted=1910.000, Expected=1964.000\n",
            ">Predicted=1964.000, Expected=2033.000\n",
            ">Predicted=2033.000, Expected=1518.000\n",
            ">Predicted=1518.000, Expected=1410.000\n",
            ">Predicted=1410.000, Expected=1431.000\n",
            ">Predicted=1431.000, Expected=1850.000\n",
            ">Predicted=1850.000, Expected=2756.000\n",
            ">Predicted=2756.000, Expected=4530.000\n",
            ">Predicted=4530.000, Expected=5217.000\n",
            ">Predicted=5217.000, Expected=4676.000\n",
            ">Predicted=4676.000, Expected=4705.000\n",
            ">Predicted=4705.000, Expected=2609.000\n",
            ">Predicted=2609.000, Expected=1477.000\n",
            ">Predicted=1477.000, Expected=1300.000\n",
            ">Predicted=1300.000, Expected=1288.000\n",
            ">Predicted=1288.000, Expected=1320.000\n",
            ">Predicted=1320.000, Expected=1640.000\n",
            ">Predicted=1640.000, Expected=2288.000\n",
            ">Predicted=2288.000, Expected=2981.000\n",
            ">Predicted=2981.000, Expected=2691.000\n",
            ">Predicted=2691.000, Expected=2404.000\n",
            ">Predicted=2404.000, Expected=2338.000\n",
            ">Predicted=2338.000, Expected=2420.000\n",
            ">Predicted=2420.000, Expected=2404.000\n",
            ">Predicted=2404.000, Expected=2142.000\n",
            ">Predicted=2142.000, Expected=2242.000\n",
            ">Predicted=2242.000, Expected=2113.000\n",
            ">Predicted=2113.000, Expected=1882.000\n",
            ">Predicted=1882.000, Expected=2017.000\n",
            ">Predicted=2017.000, Expected=2519.000\n",
            ">Predicted=2519.000, Expected=2671.000\n",
            ">Predicted=2671.000, Expected=4920.000\n",
            ">Predicted=4920.000, Expected=5318.000\n",
            ">Predicted=5318.000, Expected=3800.000\n",
            ">Predicted=3800.000, Expected=3432.000\n",
            ">Predicted=3432.000, Expected=2367.000\n",
            ">Predicted=2367.000, Expected=2086.000\n",
            ">Predicted=2086.000, Expected=1763.000\n",
            ">Predicted=1763.000, Expected=1333.000\n",
            ">Predicted=1333.000, Expected=1343.000\n",
            ">Predicted=1343.000, Expected=1387.000\n",
            ">Predicted=1387.000, Expected=1578.000\n",
            ">Predicted=1578.000, Expected=1583.000\n",
            ">Predicted=1583.000, Expected=1525.000\n",
            ">Predicted=1525.000, Expected=1328.000\n",
            ">Predicted=1328.000, Expected=1245.000\n",
            ">Predicted=1245.000, Expected=1371.000\n",
            ">Predicted=1371.000, Expected=1468.000\n",
            ">Predicted=1468.000, Expected=1278.000\n",
            ">Predicted=1278.000, Expected=1050.000\n",
            ">Predicted=1050.000, Expected=1096.000\n",
            ">Predicted=1096.000, Expected=1195.000\n",
            ">Predicted=1195.000, Expected=1018.000\n",
            ">Predicted=1018.000, Expected=1190.000\n",
            ">Predicted=1190.000, Expected=1152.000\n",
            ">Predicted=1152.000, Expected=2336.000\n",
            ">Predicted=2336.000, Expected=1755.000\n",
            ">Predicted=1755.000, Expected=2886.000\n",
            ">Predicted=2886.000, Expected=4110.000\n",
            ">Predicted=4110.000, Expected=4214.000\n",
            ">Predicted=4214.000, Expected=4095.000\n",
            ">Predicted=4095.000, Expected=2371.000\n",
            ">Predicted=2371.000, Expected=1481.000\n",
            ">Predicted=1481.000, Expected=1173.000\n",
            ">Predicted=1173.000, Expected=1179.000\n",
            ">Predicted=1179.000, Expected=1410.000\n",
            ">Predicted=1410.000, Expected=1929.000\n",
            ">Predicted=1929.000, Expected=1781.000\n",
            ">Predicted=1781.000, Expected=1533.000\n",
            ">Predicted=1533.000, Expected=1791.000\n",
            ">Predicted=1791.000, Expected=1817.000\n",
            ">Predicted=1817.000, Expected=1533.000\n",
            ">Predicted=1533.000, Expected=1263.000\n",
            ">Predicted=1263.000, Expected=959.000\n",
            ">Predicted=959.000, Expected=979.000\n",
            ">Predicted=979.000, Expected=1211.000\n",
            ">Predicted=1211.000, Expected=1263.000\n",
            ">Predicted=1263.000, Expected=1762.000\n",
            ">Predicted=1762.000, Expected=1941.000\n",
            ">Predicted=1941.000, Expected=2252.000\n",
            ">Predicted=2252.000, Expected=3675.000\n",
            ">Predicted=3675.000, Expected=3567.000\n",
            ">Predicted=3567.000, Expected=6415.000\n",
            ">Predicted=6415.000, Expected=9876.000\n",
            ">Predicted=9876.000, Expected=4936.000\n",
            ">Predicted=4936.000, Expected=2950.000\n",
            ">Predicted=2950.000, Expected=2100.000\n",
            ">Predicted=2100.000, Expected=1100.000\n",
            ">Predicted=1100.000, Expected=794.000\n",
            ">Predicted=794.000, Expected=961.000\n",
            ">Predicted=961.000, Expected=916.000\n",
            ">Predicted=916.000, Expected=1155.000\n",
            ">Predicted=1155.000, Expected=1950.000\n",
            ">Predicted=1950.000, Expected=3420.000\n",
            ">Predicted=3420.000, Expected=3200.000\n",
            ">Predicted=3200.000, Expected=3060.000\n",
            "RMSE: 924.774\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H3z_HDnwuYg3"
      },
      "source": [
        "We got a RMSE value of 924.774 using the baseline model. So, our other models must be definitely better than this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zK_Diyca5ZoM",
        "outputId": "8cf520e8-c581-44e9-c2af-590be7bc86a9"
      },
      "source": [
        "series.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     204.000000\n",
              "mean     1621.764706\n",
              "std      1236.655108\n",
              "min       384.000000\n",
              "25%       878.750000\n",
              "50%      1263.000000\n",
              "75%      1953.500000\n",
              "max      9876.000000\n",
              "Name: 1, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JHBHpJOulBV"
      },
      "source": [
        "## Visualizing our data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "xvCpc0G65bHY",
        "outputId": "08860fb7-023d-4513-e8b4-0810f833c993"
      },
      "source": [
        "plt.figure(figsize=(20,8))\n",
        "series.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff7ba78a390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "eHt47N6L52qO",
        "outputId": "3a7903bd-8de0-4aa7-9c03-97d120577ab9"
      },
      "source": [
        "plt.figure(figsize=(20,8))\n",
        "plt.figure(1)\n",
        "plt.subplot(121)\n",
        "series.hist()\n",
        "plt.subplot(122)\n",
        "series.plot(kind='kde')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff7ba78e550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "OixsM7E-6emq",
        "outputId": "39721a74-4c57-4d0c-c9b5-4b709fcb9d80"
      },
      "source": [
        "groups = series['2004':'2020'].groupby(pd.Grouper(freq='A'))\n",
        "years = pd.DataFrame()\n",
        "for name, group in groups:\n",
        "  years[name.year] = group.values\n",
        "plt.figure(figsize=(20,8))\n",
        "years.boxplot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff7ba693310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zgN9OzzWuvM-"
      },
      "source": [
        "## ADF test:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnbMta3y75pN",
        "outputId": "06ce2746-4a3b-4cc1-e9fd-517598a4d8c1"
      },
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "\n",
        "def difference(dataset):\n",
        "  diff = list()\n",
        "  for i in range(1, len(dataset)):\n",
        "    value = dataset[i] - dataset[i - 1]\n",
        "    diff.append(value)\n",
        "  return pd.Series(diff)\n",
        "\n",
        "# difference data\n",
        "stationary = difference(X)\n",
        "stationary.index = series.index[1:]\n",
        "# check if stationary\n",
        "result = adfuller(stationary)\n",
        "print('ADF Statistic: %f' % result[0])\n",
        "print('p-value: %f' % result[1])\n",
        "print('Critical Values:')\n",
        "for key, value in result[4].items():\n",
        "  print('\\t%s: %.3f' % (key, value))\n",
        "# save\n",
        "stationary.to_csv('stationary.csv', header=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ADF Statistic: -7.901252\n",
            "p-value: 0.000000\n",
            "Critical Values:\n",
            "\t1%: -3.465\n",
            "\t5%: -2.877\n",
            "\t10%: -2.575\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LL_N4ef19uV7"
      },
      "source": [
        "ADF (Augmented Dicky Fuller) statistic is -7.9 which is less than 1% critical value. So, we can reject the null hypothesis and accept the alternate hypothesis (which is the data is stationary, by performing 1 differencing)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mb2vHszjvB-3"
      },
      "source": [
        "## Plotting Autocorrelation and Partial Autocorrelation plots:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "V-sX5onI80f8",
        "outputId": "8b44e63f-f849-4cf8-9152-f056f5b16bd9"
      },
      "source": [
        "from statsmodels.graphics.tsaplots import plot_acf\n",
        "from statsmodels.graphics.tsaplots import plot_pacf\n",
        "\n",
        "plt.figure(figsize=(20,8))\n",
        "plt.subplot(211)\n",
        "plot_acf(series, lags=50, ax=plt.gca())\n",
        "plt.subplot(212)\n",
        "plot_pacf(series, lags=50, ax=plt.gca())\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iOQP9XLZkfhtbJUmS1NnKmJPoKmBrZm4DiIh7geuAzU31/hPwx8DvlHBNdRjnfpAkdZLVlyxm5bIFfO3bz5B9s5g7+zRWLlvA6ksWn/I5vRkiSZK6XRkh0fnA9obtHcDVjRUi4lXAssz83xFhSNSFGud+gJFzP1xz6bltbp0kaSYoM4Tp7wvuWXM1r/mZNQydsZgPvfddLZ3PmyGSJKkXTPnqZhHRB/wJcNNJ1F0LrAVYvnz51DZMpZpo7gdDIknSiUxFCNPfF8wd3MbcwW0t/1/kzRBJktQLypi4eiewrGF7aVFWdybw/cD6iHgSeDWwbqzJqzPz9sxclZmrFi1aVELTNF3qcz80cu4HSdLJ6vTVyJwIW5Ik9YIyQqKHgYsj4sKIGACuB9bVd2bmS5m5MDNXZOYK4OvAmzJzQwnXVoeoz/0QlSHIKnOLO8CtzP0gSeodnR7CeDNEkiT1gpZDoswcBm4GHgAeB+7LzE0RcVtEvKnV82tmqM/9sOiJf2DBjq/yZzdc6TwNkqST1ukhjDdDJElSLyhlTqLMvB+4v6ns1nHqri7jmuo8Zc79IEnqLVOxGlmZyp4IW5IkqRNN+cTVkiRJJzITQhhvhkiSpG5nSCRJkjqCIYwkSVJ7GRJJkiRJkqSekJlkQtYfQ7FdKz9e73hZAv0Ro+ZP7EaGRJIkSZLaJotPZfUPZ9lU3lh2/Jim7YYazftGX+8k2jTqiqd+rm7Uo0+7VFnyL8+JXiNjVRrr93y812GO2p/HCsfc13SuejBTL28MXxqDGkaUN9RrCnFGbZ/oPCP2nZoFc0/j0vM6Y0GNqWRIJEmSNENVq0k1k2pSfK89PvZmuDr+G/H6G/xRb8SL89JYj5FvrCf6ED/yDfj4x56MXvgAfrJhxKjjmj/sneDD38kGJ2N9uKttj/ygOPKcE/2OjH18L/zbStJMZEgkSZI0xSrVZLhaLb4nlUrt+9Bwla9+53m2PPsyFy0+g1UXnE1EkJlUMqlWR4Y/1cwiGKo99oO2JEkqkyGRJEnSCVSrydEi5EnghQNDx0OfSh4Pf5rCoGrxfawwp1pNPvC5x9m6ez9Dw1UGZvVx0eJ5vPsNl9LXQau6SZKk3mFIJKlrVKrJ+i272bRrH5cvmd9xy2dL6gyVanK0UmW4mgxXqhwtQp7msuFqfbu2H+DAkWEAtjz7csvt2Lh9kK2793NkuArAkeEqW3fvZ+P2QV51wVktn1+SJGmyDIkkdYVKNXnbHQ+xcfsgh4YqzBnoZ+WyBdyz5mqDImmKtDOYHW/4Vr18uNg+OFQhSR7bPnisvNohQ7Se3HuAoSIgqhsarvLk3gOGRJIkqS0MiSR1hfVbdrNx+yAHhyoAHByqsHH7IOu37OaaS889pXPaM0ka38kGs9k8qXJ19ATL1aydL7M2R08CT+89ODIEOvZ9ckHPcKUWwtT/NnSSFeecwcCsvmM9iQAGZvWx4pwz2tgqSZLUywyJJHWFTbv2cajpQ+ChoQqbd+07pZBoJvZMqi8tWl/BCEavWnS87siVjI4dT8OKM00r3cDo1W7qx40uG33s8Z0nVTTiPCPrnnw3kLIn9W1XB5SJlsmd7HMcawWjsX5nGLX/+HGZ8PVte3n06Rc5fPR4CPPo0y9yx79s44cuOKsh+Jlc+w4frb2Odw4emtyBM9DKZQu4aPE8Nj39PPTPYvZps7ho8TxWLlvQ7qZJktQTqtVk4/ZBntx7gBXnnMHKZQt6fl5AQyJJXeHyJfOZM9A/orfAnIF+Llsy/5TOd6KeSZm1ng2NKwxVi9WIcoyeEpV6b4rq8brJ8WMbA5rG88HI7WNLWCcjl7nukOEz6h2bd+3jyNGRQ6WOHK3yxHP7+YHzDTlORl9f8O43XMov/fpvUZl3Ljf/8lrfnEqSNE1cQGJshkSS2qbM4VyrL1nMymUL+Nq3nyH7ZjF39mn84NJXsGrF2bx06OiIUKc5tKmOse/LW/aM2TPp85uf44zZswxl1PMcKlWOvr5gYO9W2LuVV13we+1ujiRJPcMFJMZmSCR1kfGGpLQyRGjM4T5NQ5VG9GYZY1+1KKNh2Mpwtcqv37uRzbv2cfhohdNP6+PS8+bzX372CiKa5jGpjuxVc7z3TmPQA7+6+iK++fd/TmXeufxqcUd+8659p/CThPPmnz7mB+DlZ801IJJwqJQkSZp+ZQ4PcwGJsRkSqeNUq8lQ0zLE9RDi2NCcoh7NZScxdKcecNQfw9jhyejy0aXNYcFE2cF4wcKEc41M8lwzyaNPvci3dr50LIQ5dLTKpl37eOBbz57yH+Uy78j7AViamEOlJEnSdCp7eJi9osdmSKQpd7RSW4nmaH1J4kqVo9Xk6HCV4WotBGrcX+mUtYk1pTo9uZ+KD8BOjKdu41ApSZI0XcoeHuZN4bEZEqkUh4Yq7D8yzOGjFaoJj20fPBYAdUOvF5VvJiT3ZX4AdmI8SZIk6dSVfZPZXtFjMyTSpNUDoYNDw8X3CsOVWhJUf9EebJrw91TZ86J79Vpy78R4kiRJ0qmbipvM9ooerZSQKCKuBf4U6Af+Z2Z+sGn/bwLvAIaBPcD/nZlPlXFtTa1DQxUODA1z4MjoQGiq2fOiu/Vact/pw+skdQdvrkiSulWv3WRul5ZDoojoBz4OvB7YATwcEesyc3NDtW8AqzLzYET8e+C/AG9t9doq1+GjtR5C7QiExmLPi+7XS8n9TBheJ2lm8+aKJKmTlH3jotduMrdLGT2JrgK2ZuY2gIi4F7gOOBYSZeaXG+p/HfiFEq6rFh0ZrjA0XFtF7OEnX2hrIDQWe16om3jnQ9JU8+aKJKlTTNWNi166ydwufSWc43xge8P2jqJsPGuAz5VwXZ2CI8MVnnnpEN/a+RKPPjXI4aMVhovVxzpNvedFI3tetFe1mjz61Iv83aM7ePSpF6m6Et1Jq9/5mLf5s8z57j/zH153sXf3JZVqopsrkiRNp8YbF8nIGxfqbNM6cXVE/AKwCvjRcfavBdYCLF++fBpb1t2ODFd44cAQe/cP8fLh4XY356TZ86KzOIyhdd75kDSVHNYqSeoUjgqZucroSbQTWNawvbQoGyEifhx4D/CmzDwy1oky8/bMXJWZqxYtWlRC03pXc4+hJ58/OKMCIrDnRafxbkDnsWeXpEb1mysMD0FWmV2E+d5ckSRNN0eFzFxl9CR6GLg4Ii6kFg5dD/xcY4WIuBL478C1mbm7hGtqDDO1x9BE7HnRObwb0Fns2aVT4cpX3c0JPSVJncJRITNXyyFRZg5HxM3AA0A/cGdmboqI24ANmbkO+K/APOBvIgLg6cx8U6vXVi0YevHAUZ7ff6RrgiF1JocxdBYnqNVkGSz2Bm+uSJJOVZk3k7xxMXOVMidRZt4P3N9UdmvD4x8v4zqqqWby7EuHDYY0rbwb0Fns2aXJMliUJEnjmYqbSd64mJnKmJNI0+DIcIVnXzrMgSPD7D8yzHefP2BApGnlHFGdxXHemixXvpI01ZwrT5q5nH9UddO6upkmZ2i4ygsHhnh+/xH2HxkmEyr+Z6s28m5A57BnlybLIaOSmpU5tMQhrdLMZi911RkSdZixgiFJauY4b02WwaJOhZOdd6+yQx2HtEozmzeTVGdI1EG27t7P8/uPGAypNL65725T0bPL35nuZbCoybJnSHcrO9SxF4I0s3kzSXWGRB3k8NGKAdEU66UPwL6512T5O9P9HDKqybBnSHcrO9SxF4I0s3kzSXWGROoZvfYB2Df3mix/ZyQ1smdI5ynzZlfZoc5U9ELopZt7UifwZpLAkEg9pNc+APvmXpPl74ykRvYM6Sxl3+wqO9QpuxdCr93ck06FQaqmQt+Jq0jdodeWf3aJdE2WvzOSGtVDBIaHIKvMLj6kOz9Fe5S9PHU91Jm3+bPM+e4/8x9ed3HLAUy9F8Kcp77Kqy44q6VzuRy3NLF6kPrRLz3BZx7ZwUe/9AQf+NzjVF0NWy0yJFLP6LUPwL6512RNxe9MtZo8+tSL/N2jO3j0qRd94yLNIFMRIujUTcXNrjJDnbL12s09abIMUjVVHG6mnjETxsqXeT4nn9NkOVSg89iNXO3m/BSdo9eG//Xa85Umy2kCNFUMidQzOv0D8FR8oPbNvSarzN+ZXpsHrGyGbJIa9dry1FP1fA3f1S0MUjVVHG6mntLJY+XtMqpu41CB1vg3QZr5yhxy22vD/6bi+TqHi7qJU0toqhgSSaeo7A/AfqBWt5mKecB6aY4j/yZIM9tUBBKdPIfQVCj7+Rq+q5v0WnCs6eNwM+kUld3F0y6j6jZlDxXoteFX/k1QN+qloT4Oue08zuGidiv7b6BTS2gqGBJJp6jsD8C9NteAul/Z84BNxQeuTp583r8J6ja9FvQaSHQew3e1U6/9DdTMZUgknaKyPwC7Gpm6UZl3uMr+wNXpk8/7N0Hdptd61hhIdJ6ZsNKtulev/Q3UzOWcRFILyh4r32tzDUiTUfYcRzNh8nn/Jqib9No8W04q23nKnsPFibA1Gb32N1AzlyGRJGlGKPsDl5PPS9NrKiazL5urkXW/Tl7pVt1tJvwNlKCkkCgiro2ILRGxNSJuGWP/7Ij4dLH/oYhYUcZ1JUm9o+wPXGW/WfPNnzSxTu9Z42pkmixvDnS/MoPjTv8bKNW1HBJFRD/wceANwGXADRFxWVO1NcCLmXkR8GHgj1u9riSp95T5gavsN2u++ZMmNhU9a8r8AGevEE3WVNwcKPN3Wq0pOzi2d6FmijImrr4K2JqZ2wAi4l7gOmBzQ53rgPcXjz8DfCwiIjP9qydJagsnn5emX5mT2Zc9WbyrkWmyyp4I29WvOstUTDTtkvWaCaLVnCYifha4NjPfUWy/Dbg6M29uqPOtos6OYvs7RZ3nxzvv2Rdcmq9/950tta0TbHxsIwArr1h5wroHhoapnCCZfmLztwC4+LLvb71xM+B8U3FOz+f5PN/0ntPzeT7PN73n7JXzvXx4mJ2Dh2h8KxsB5y+Yw5mnT/4+aNnnq+vUn1+vnq/sc2Ym3966DfoHWLLkPObN7ifi1AKdqfgdzEz2H6lw+GiF00/rb6l9M0GZz3fPy0d4fv/QqPJF8wZYeObsU25jp79GOv18U3HOkz3frL4+5g70l3LNdrvvl//NI5m5aqx9HRUSRcRaYC3AvPO+94fe+L57WmrbTHMyIZEkSZLK/wCXmTz9wiEOHa2QWftwPue0fpafPaerP1Src0zV7/TBI0eBIPqilN/pTg0Ryn6+UxUca+bqlZCojN/uncCyhu2lRdlYdXZExCzgFcDe5hNl5u3A7QCrVq3KT//Sa0po3szxrZ0v8fLh4XY3Q5IkqeM9+tSLfPRLTxwbCgIwe1YfN/2bC095KEi1mmzcPsiTew+w4pwzHDKqaVX273T9fERt3qRMqFSTn/7B81t6jfzSA3dQmXcuP/UDP9Hya6TM85X9fB3+p2YL5p7GpefNb3czSnHfL4+/r4yQ6GHg4oi4kFoYdD3wc0111gE3Al8Dfhb4kvMRSZIk6VTV54Np/gDXymTxfX3Bqy44yzmI1BZl/06XPc9WPTTZf9mboX8WH/3SEy2FJmWfr+znW59r0OBYvablkCgzhyPiZuABoB+4MzM3RcRtwIbMXAfcAdwTEVuBF6gFSZIkSdIp8QOcuk3Zv9P11dcaeya1svpafSJnZg0ArU/kXPb5yn6+YHCs3lTKYMrMvB+4v6ns1obHh4H/q4xrSZIkSeAHOHWfMn+nO71nUtnnm4rehVIvcsYtSZIkSeoynd4zqezz2btQKochkSRJkiR1oU7umeS8YlJnMiSSJEmSJE2o7J469vyROpMhkSRJkiTphMruqWPPH6nz9LW7AZIkSZIkSWo/QyJJkiRJkiQZEkmSJEmSJMmQSJIkSZIkSRgSSZIkSZIkCUMiSZIkSZIkYUgkSZIkSZIkDIkkSZIkSZKEIZEkSZIkSZIwJOooFy2ex/Jz5nLG7P52N0WSJEmSJPWYWe1ugI47/bR+zl8wh/MXzOHw0QrP7z/CCweGOHCk0u6mSZIkSZKkLmdI1KFOP62fpWfNZelZcw2MJEmSJEnSlDMkmgEMjCRJkiRJ0lQzJJphDIwkSZIkSdJUaGni6hWdWlcAACAASURBVIg4OyK+EBFPFN/PGqPOyoj4WkRsiohvRsRbW7mmjqsHRj+4dAFXLl/AsrPnOOm1JEmSJEk6Ja2ubnYL8GBmXgw8WGw3Owi8PTMvB64FPhIRC1q8rpoYGEmSJEmSpFa0OtzsOmB18fhuYD3we40VMvPbDY93RcRuYBEw2OK1NQ6HpEmSJEmSpMlqNSQ6NzOfKR4/C5w7UeWIuAoYAL4zzv61wFqA5cuXt9g0gYGRJEmSJEk6OScMiSLii8Arx9j1nsaNzMyIyAnOcx5wD3BjZlbHqpOZtwO3A6xatWrcc+nUNAZGh4Yq7D1gYCRJkiRJkmpOGBJl5o+Pty8inouI8zLzmSIE2j1OvfnA/wbek5lfP+XWqjRzBvpZOnC8h9GBI8McOFJh/5FhDg4Nc7RiRidJkiRJUi9pdbjZOuBG4IPF9//VXCEiBoC/B/4qMz/T4vU0BU4/rZ/TT+vnnHnHy5qDowNDwwwbHEmSJEmS1LVaDYk+CNwXEWuAp4C3AETEKuCXM/MdRdm/Bc6JiJuK427KzI0tXltTyOBIkiRJkqTe0lJIlJl7gWvGKN8AvKN4/AngE61cR53B4EiSJEmSpO7Vak8i9bixgqMjwxWOVpKjw1WOVqsMV5LhSh57fLRS5WilynA1DZQkSZIkSeoQhkQq3exZ/cyeBcw+cd3M5GglGa5WOTo8MkgarhbfK0k1k0xIkmrWjsvi+Eyayqb4CUqSJEmS1IUMidRWEcHArGCAPhgo55zHg6PjoVG1SI6Oh00j6x97PKJ8dGE2bNT3j5dJ5QRp1UQ51niH5XhHnWRxjvE8RpbV643e13yO5PjPcWQ411jeUK/hMc37aPh3yVoIWN+WJEmSJE0fQyJ1nYggAvqIdjdFLWgMjOqhUbUxRKoeDwLHrFOtlVWqx3ufVTKpVo/Xr++rZhb12/2sJUmSJKl9DIkkdaSIoD+gfxrDvsZgqlKthU6VegBVhEuVCQKp472gao8r1dF1a+ctwq2qQyQlSZIkdQ5DIkkqNAZTp/VP33VPZojksX3VkXNzVYuhe2TjkMGRw/kar1HfD2MPGzy+f+KhlY3naNwea1/z/hP/PMYom3CQ5qkFbe3K5iYainrS5xj33Cd/ROPQ0Hq7DC0lSZJ6myGRJLWZQyTVierB4oigkdEhIxwPNsfqXXesZ10xrHNk3cYhnyPPUanWFy5ox7OXJEnqTYZEkiRplHp42VDSlnZUqrUVMGvfk0ql+N5QfrSSo+tVk+GKCZMkSdJkGBJJkqSO1d8X9Ped2vjPzDwWGg1Xk+FKlaOVWpg0XBldVg+bKnZfkiRJPcqQSJIkdaWIYFZ/MGuSGVO1mhytB0mV2uNaj6V6uFQtAqYiVEp7LkmSpO5gSCRJktSgry+Y3dfP7Em+S2rsuTTye/VYiNQcKtX3H5+byXmYJElS+xgSSZIkleBUey41y8YJvZsn9W54XGlY/bBSbXrM8ZUKG1eyO7aqYcMqis0rIo6cpLx5AvOR7azVaCxr7blLkqT2MiSSJEnqIBFBf0D/DF7xMBvSouMr4o29v5TrlXq2znEyP6ZsevYnOqZ5f/34EQHgsbrZtM2oSs3Hj/Xv3Hx8MrryeHVGt7f5+Yz+XRvPePubf4aTPX4yuvV3dTqV/fej00322Y7945n478To11W9vOn1PcHrc9xjju2f4AZG0wqqai9DIkmSJJUqGpbGizGzrpkbgEmSpk7m2D1b6z1eadoe1QN2gn2NAfWoHrYN1x7vGnMGWuwqPEMYEkmSJEmSpLaLiIabC95QaIe+djdAkiRJkiRJ7WdIJEmSJEmSpNZCoog4OyK+EBFPFN/PmqDu/IjYEREfa+WakiRJkiRJKl+rPYluAR7MzIuBB4vt8fwn4CstXk+SJEmSJElToNWQ6Drg7uLx3cCbx6oUET8EnAt8vsXrSZIkSZIkaQq0GhKdm5nPFI+fpRYEjRARfcCHgN9u8VqSJEmSJEmaIrNOVCEivgi8coxd72ncyMyMiByj3q8A92fmjoiJl7CLiLXAWoDly5efqGmSJEmSJEkqyQlDosz88fH2RcRzEXFeZj4TEecBu8eo9hrgRyLiV4B5wEBE7M/MUfMXZebtwO0Aq1atGitwkiRJkiRJ0hSIzFPPYiLivwJ7M/ODEXELcHZm/u4E9W8CVmXmzSdx7j3AU6fcuM6yEHi+3Y2QOpivEWlivkakifkakSbma0SaWK+9Ri7IzEVj7ThhT6IT+CBwX0SsoRbovAUgIlYBv5yZ7zjVE4/X4JkoIjZk5qp2t0PqVL5GpIn5GpEm5mtEmpivEWlivkaOaykkysy9wDVjlG8ARgVEmXkXcFcr15QkSZIkSVL5Wl3dTJIkSZIkSV3AkGh63N7uBkgdzteINDFfI9LEfI1IE/M1Ik3M10ihpYmrJUmSJEmS1B3sSSRJkiRJkiRDoqkWEddGxJaI2BoRt7S7PVK7RcSdEbE7Ir7VUHZ2RHwhIp4ovp/VzjZK7RIRyyLiyxGxOSI2RcSvF+W+RiQgIk6PiP83Ih4rXiN/UJRfGBEPFe+3Ph0RA+1uq9ROEdEfEd+IiH8stn2NSIWIeDIi/jUiNkbEhqLM91oFQ6IpFBH9wMeBNwCXATdExGXtbZXUdncB1zaV3QI8mJkXAw8W21IvGgZ+KzMvA14N/Grx/4avEanmCPC6zLwCWAlcGxGvBv4Y+HBmXgS8CKxpYxulTvDrwOMN275GpJF+LDNXNix773utgiHR1LoK2JqZ2zJzCLgXuK7NbZLaKjO/ArzQVHwdcHfx+G7gzdPaKKlDZOYzmflo8fhlam/wz8fXiARA1uwvNk8rvhJ4HfCZotzXiHpaRCwFfhL4n8V24GtEOhHfaxUMiabW+cD2hu0dRZmkkc7NzGeKx88C57azMVIniIgVwJXAQ/gakY4phtFsBHYDXwC+Awxm5nBRxfdb6nUfAX4XqBbb5+BrRGqUwOcj4pGIWFuU+V6rMKvdDZCkRpmZEeGyi+ppETEP+FvgNzJzX+0mcI2vEfW6zKwAKyNiAfD3wP/R5iZJHSMifgrYnZmPRMTqdrdH6lCvzcydEbEY+EJE/H+NO3v9vZY9iabWTmBZw/bSokzSSM9FxHkAxffdbW6P1DYRcRq1gOivM/PvimJfI1KTzBwEvgy8BlgQEfWbn77fUi/7YeBNEfEktakuXgf8Kb5GpGMyc2fxfTe1mw1X4XutYwyJptbDwMXFagIDwPXAuja3SepE64Abi8c3Av+rjW2R2qaYN+IO4PHM/JOGXb5GJCAiFhU9iIiIOcDrqc3d9WXgZ4tqvkbUszLz9zNzaWauoPbZ40uZ+fP4GpEAiIgzIuLM+mPgJ4Bv4XutYyKzZ3tRTYuIeCO1ccH9wJ2Z+UdtbpLUVhHxKWA1sBB4Dngf8FngPmA58BTwlsxsntxa6noR8Vrgn4F/5fhcEu+mNi+RrxH1vIj4QWoTivZTu9l5X2beFhHfQ63XxNnAN4BfyMwj7Wup1H7FcLPfzsyf8jUi1RSvhb8vNmcBn8zMP4qIc/C9FmBIJEmSJEmSJBxuJkmSJEmSJAyJJEmSJEmShCGRJEmSJEmSMCSSJEmSJEkShkSSJEmSJEnCkEiSJEmSJEkYEkmSJEmSJAlDIkmS1MEiYn9EfM9J1FsRERkRs6ajXZ0qIm6KiH9p4fjPRcSNZbZJkiTNHIZEkiTplEXEkxFxqAhznouIuyJi3imea31EvKOxLDPnZea2clp77BovRsTsSR6XEXFRWe3oBBHx/oj4RGNZZr4hM+9uV5skSVJ7GRJJkqRW/XRmzgNeBawC3juZg6Nmyt+TRMQK4EeABN401ddr1Vi9onq9p5QkSZpahkSSJKkUmbkT+Bzw/RFxVkT8Y0TsKXru/GNELK3XLXr0/FFEfBU4CNxDLcD5WNEr6WNFvWM9eCLiJyPiGxGxLyK2R8T7J9nEtwNfB+4CRgypau7F1DhsKyK+UhQ/VrTtrUX5OyNia0S8EBHrImJJw/GXR8QXin3PRcS7i/LZEfGRiNhVfH2k3qspIlZHxI6I+L2IeBb4y6K3z2ci4hMRsQ+4KSJeERF3RMQzEbEzIv4wIvrHesIR8afFz2pfRDwSET9SlF8LvBt4a/GcHmv+OUREX0S8NyKeiojdEfFXEfGKYl99eN+NEfF0RDwfEe+Z5L+HJEnqMIZEkiSpFBGxDHgj8A1q7zH+ErgAWA4cAj7WdMjbgLXAmcBNwD8DNxdDzG4e4xIHqAU9C4CfBP59RLx5Ek18O/DXxde/i4hzT+agzPy3xcMrirZ9OiJeB/xn4C3AecBTwL0AEXEm8EXgn4AlwEXAg8U53gO8GlgJXAFcxcieV68Ezqb2c1tblF0HfKZ43n9NLeQaLs57JfATwIhheg0eLq51NvBJ4G8i4vTM/CfgA8Cni+d0xRjH3lR8/RjwPcA8Rv8bvha4BLgGuDUiLh2nHZIkaQYwJJIkSa36bEQMAv8C/D/ABzJzb2b+bWYezMyXgT8CfrTpuLsyc1NmDmfm0RNdJDPXZ+a/ZmY1M78JfGqMc44pIl5LLXi5LzMfAb4D/NzJP8VRfh64MzMfzcwjwO8DrymGtP0U8GxmfigzD2fmy5n5UMNxt2Xm7szcA/wBtbCsrgq8LzOPZOahouxrmfnZzKwC86kFcb+RmQcyczfwYeD6sRqZmZ8o/i2GM/NDwGxqoc7JPsc/ycxtmbm/eI7XNw15+4PMPJSZjwGPUQu+JEnSDOW4dkmS1Ko3Z+YXGwsiYi618OJa4Kyi+MyI6M/MSrG9fTIXiYirgQ8C3w8MUAs8/uYkD78R+HxmPl9sf7Io+/Bk2tBgCfBofSMz90fEXuB8YBm1EGq8455q2H6qKKvbk5mHm45p/DldAJwGPBMR9bI+xvlZRsRvA2uKayS1kGnhuM/qxG2dBTT2wHq24fFBar2NJEnSDGVPIkmSNBV+i1qPlaszcz5QH7IVDXWy6Zjm7WafBNYByzLzFcBfNJ1vTBExh9qwsB+NiGeL+X7eBVwREfWeLweAuQ2HvfIEp91FLbCpX+MM4BxgJ7XA5ntO5jhqQ/F2NWyP9TNoLNsOHAEWZuaC4mt+Zl7efFAx/9DvUnvuZ2XmAuAljv/MTvTzHqutw8BzJzhOkiTNUIZEkiRpKpxJbR6iwYg4G3jfSRzzHOOHK/VzvpCZhyPiKk5+uNibgQpwGbX5eVYCl1KbA+ntRZ2NwM9ExNxiouw1J2jbp4BfjIiVxcTTHwAeyswngX8EzouI3ygmqj6z6AVVP+69EbEoIhYCtwIjlqGfSGY+A3we+FBEzC8ml/7eiBhr2N2Z1EKdPcCsiLiVWk+ixue0YoKV5T4FvCsiLoyIeRyfw2j4ZNsrSZJmFkMiSZI0FT4CzAGep7ai2D+dxDF/CvxssRraR8fY/yvAbRHxMrVw5b6TbMuNwF9m5tOZ+Wz9i9okzD9fzLHzYWCIWnByN7UJohu9H7g7IgYj4i3F8Lr/CPwt8AzwvRTzAhVzML0e+Glqw7GeoDb5M8AfAhuAbwL/Sm3I2h+e5POoezu14XabgRepTWp93hj1HqD2c/82taFihxk5LK0+VG9vRDzKaHdSW3XuK8B3i+N/bZJtlSRJM0hknqinsSRJkiRJkrqdPYkkSZIkSZJkSCRJkiRJkiRDIkmSJEmSJGFIJEmSJEmSJAyJJEmSJEmSBMxqdwPGs3DhwlyxYkW7myFJkiRJktQ1Hnnkkeczc9FY+zo2JFqxYgUbNmxodzMkSZIkSZK6RkQ8Nd4+h5tJkiRJkiTJkEiSJEmSJEmGRJIkSZIkSaKkkCgi7oyI3RHxrXH2R0R8NCK2RsQ3I+JVZVxXkiRJkiRJ5SirJ9FdwLUT7H8DcHHxtRb485Ku29Eq1eTBx5/jow8+wYOPP0elmu1ukiRJkiRJ0phKWd0sM78SESsmqHId8FeZmcDXI2JBRJyXmc+Ucf1OVKkmb7vjITZuH+TQUIU5A/2sXLaAe9ZcTX9ftLt5kiRJkiRJI0zXnETnA9sbtncUZV1r/ZbdbNw+yMGhCgkcHKqwcfsg67fsbnfTJEmSJEmSRumoiasjYm1EbIiIDXv27Gl3c1qyadc+Dg1VRpQdGqqwede+NrVIkiRJkiRpfNMVEu0EljVsLy3KRsjM2zNzVWauWrRo0TQ1bWpcvmQ+cwb6R5TNGejnsiXz29QiSZIkSZKk8U1XSLQOeHuxytmrgZe6eT4igNWXLGblsgVEZQiyytxiTqLVlyxud9MkSZIkSZJGKWXi6oj4FLAaWBgRO4D3AacBZOZfAPcDbwS2AgeBXyzjup2svy+4Z83VvOZn1jB0xmI+9N53sfqSxU5aLUmSJEmSOlJZq5vdcIL9CfxqGdeaSfr7grmD25g7uI1rLj233c2RJEmSJEkaV0dNXC1JkiRJkqT2MCSSJEmSJEmSIZEkSZIkSZIMiSRJkiRJkoQhkSRJkiRJkjAkkiRJkiRJEoZEkiRJkiRJwpBIkiRJkiRJGBJJkiRJkiQJQyJJkiRJkiRhSCRJkiRJkiQMiSRJkiRJkoQhkSRJkiRJkjAkkiRJkiRJEjCr3Q3QyatUk/VbdrNp1z4uXzKf1Zcspr8v2t0sSZIkSZLUBQyJZohKNXnbHQ+xcfsgh4YqzBnoZ+WyBdyz5mqDIkmSJEmS1DKHm80Q67fsZuP2QQ4OVUjg4FCFjdsHWb9ld7ubJkmSJEmSuoAh0Qyxadc+Dg1VRpQdGqqwede+NrVIkiRJkiR1E0OiGeLyJfOZM9A/omzOQD+XLZnfphZJkiRJkqRuUkpIFBHXRsSWiNgaEbeMsX95RHw5Ir4REd+MiDeWcd1esvqSxaxctoCoDEFWmVvMSbT6ksXtbpokSZIkSeoCLYdEEdEPfBx4A3AZcENEXNZU7b3AfZl5JXA98N9avW6v6e8L7llzNYue+AcW7Pgqf3bDlU5aLUmSJEmSSlPG6mZXAVszcxtARNwLXAdsbqiTQH1c1CuAXSVct+f09wVzB7cxd3Ab11x6brubI0mSJEmSukgZw83OB7Y3bO8oyhq9H/iFiNgB3A/82lgnioi1EbEhIjbs2bOnhKZJkiRJkiTpZEzXxNU3AHdl5lLgjcA9ETHq2pl5e2auysxVixYtmqamSZIkSZIkqYyQaCewrGF7aVHWaA1wH0Bmfg04HVhYwrUlSZIkSZJUgjJCooeBiyPiwogYoDYx9bqmOk8D1wBExKXUQiLHk0mSJEmSJHWIlkOizBwGbgYeAB6ntorZpoi4LSLeVFT7LeCdEfEY8CngpszMVq8tSZIkSZKkcpSxuhmZeT+1Cakby25teLwZ+OEyriVJkiRJkqTyTdfE1ZIkSZIkSepghkSSJEmSJEkyJJIkSZIkSZIhkSRJkiRJkjAkkiRJkiRJEoZEkiRJkiRJwpBIkiRJkiRJGBJJkiRJkiQJQyJJkiRJkiRhSCRJkiRJkiQMiSRJkiRJkoQhkSRJkiRJkjAkkiRJkiRJEoZEkiRJkiRJwpBIkiRJkiRJGBJJkiRJkiQJQyJJkiRJkiRhSCRJkiRJkiRKCoki4tqI2BIRWyPilnHqvCUiNkfEpoj4ZBnXlSRJkiRJUjlmtXqCiOgHPg68HtgBPBwR6zJzc0Odi4HfB344M1+MiMWtXleSJEmSJEnlKaMn0VXA1szclplDwL3AdU113gl8PDNfBMjM3SVcV5IkSZIkSSUpIyQ6H9jesL2jKGv0fcD3RcRXI+LrEXFtCdeVJEmSJElSSVoebjaJ61wMrAaWAl+JiB/IzMHGShGxFlgLsHz58mlqmiRJkiRJksroSbQTWNawvbQoa7QDWJeZRzPzu8C3qYVGI2Tm7Zm5KjNXLVq0qISmSZIkSZIk6WSUERI9DFwcERdGxABwPbCuqc5nqfUiIiIWUht+tq2Ea0uSJEmSJKkELYdEmTkM3Aw8ADwO3JeZmyLitoh4U1HtAWBvRGwGvgz8TmbubfXakiRJkiRJKkcpcxJl5v3A/U1ltzY8TuA3iy9JkiRJkiR1mDKGm0mSJEmSJGmGMySSJEmSJEmSIZEkSZIkSZIMiSRJkiRJkv7/9u4/yKrzvu/4+7t3tQiEyEpYUCGIkA1DQamDVUaykkxLJTuDUsfyuI4rp3GVKR7ZM1bjtu6kitxxWnfsidtJmqj1ZKqxNHWVJpai2A5N5HFdZKaejK0IYdIYKAb9wCAUQDIUS0Ks2PvtH3tX2cWw/LjP3XPuPe/XjEb3nnvmnoe75znnOZ/zPM8RhkSSJEmSJEnCkEiSJEmSJEkYEkmSJEmSJAlDIkmSJEmSJGFIJEmSJEmSJAyJJEmSJEmShCGRJEmSJEmSMCSSJEmSJEkShkSSJEmSJEnCkEiSJEmSJEkYEkmSJEmSJAlDIkmSJEmSJGFIJEmSJEmSJAyJJEmSJEmSRKGQKCI2RMTuiNgbEffMsN4/iIiMiHUltitJkiRJkqQyug6JIqIFfA64DVgDfCAi1pxhvcuBjwFPdLtNSZIkSZIklVWiJ9GNwN7MfCYzx4AvArefYb1/B3wWeK3ANiVJkiRJklRQiZDoGmD/lPcHOsveEBE3AMsy808LbE+SJEmSJEmF9Xzi6ogYAn4L+Ph5rHtXRGyNiK1HjhzpddEkSZIkSZLUUSIkeh5YNuX90s6ySZcDPwFsiYjngLcDm840eXVm3p+Z6zJz3VVXXVWgaJIkSZIkSTofJUKiJ4GVEXFdRIwAdwCbJj/MzP+XmW/KzOWZuRz4NvDuzNxaYNuSJEmSJEkqoOuQKDNPAXcDXwN2AY9k5o6I+FREvLvb75ckSZIkSVLvDZf4ksx8DHjstGWfPMu660tsU5IkSZIkSeX0fOJqSZIkSZIk1Z8hkSRJkiRJkgyJJEmSJEmSZEgkSZIkSZIkDIkkSZIkSZKEIZEkSZIkSZIwJJIkSZIkSRKGRJIkSZIkScKQSJIkSZIkSRgSSZIkSZIkCUMiSZIkSZIkYUgkSZIkSZIkDIkkSZIkSZKEIZEkSZIkSZIwJJIkSZIkSRKGRJIkSZIkScKQSJIkSZIkScBw1QWQJEmzY7ydbNl9mB0Hj3P9kgWsX7WI1lBUXSxJkiTVRJGQKCI2AL8DtIDPZ+ZvnPb5vwA+BJwCjgD/JDP3ldi2JEk6t/F28sEHnmD7/mOcGBtn7kiLtctGeWjjTQZFkiRJAgoMN4uIFvA54DZgDfCBiFhz2mrfAdZl5luBR4F/3+12JUnS+duy+zDb9x/j1bFxEnh1bJzt+4+xZffhqosmSZKkmigxJ9GNwN7MfCYzx4AvArdPXSEzv5GZr3befhtYWmC7kiTpPO04eJwTY+PTlp0YG2fnweMVlUiSJEl1UyIkugbYP+X9gc6ys9kIfLXAdiVJ0nm6fskC5o60pi2bO9JizZIFFZVIkiRJdTOrTzeLiF8C1gH/4Syf3xURWyNi65EjR2azaGqI8Xayedch7tu8h827DjHezqqLJEmzYv2qRaxdNkqMj0G2mdeZk2j9qkVVF02SJEk1UWLi6ueBZVPeL+0smyYi3gF8Avi7mXnyTF+UmfcD9wOsW7fOq3cV5aStkpqsNRQ8tPEmbn7vRsYuW8Rv/ut/7tPNJEmSNE2JkOhJYGVEXMdEOHQH8ItTV4iItwH/BdiQmc6QqUpMnbQVpk/aeuvqxRWXTpJ6rzUUzDv2DPOOPVPkuDfeTrbsPsyOg8e5fskCQydJkvQG2wn9qeuQKDNPRcTdwNeAFvBgZu6IiE8BWzNzExPDy+YDfxgRAN/PzHd3u23pQsw0aashkSRdGHtnSpKks7Gd0L9K9CQiMx8DHjtt2SenvH5Hie1I3ZictPXVKUGRk7ZK0sXpRe9M7zhKkjQYHMXRv4qERFI/mJy09Vvfe4EcGmbenEuctFWSLlLp3pnecZQkaXA4iqN/zerTzaQqTU7aetWe/8HogT/jP33gbV58SNJFmuydOVU3vTOn3nFMpt9xlCRJ/aV0O0Gzx5BIjTI5aevo89/m1tWLDYgu0Hg72bzrEPdt3sPmXYcYb/sQQqmpJntnxvgYZJt5nZ4/F9s7c6Y7jpIkqb+Ubido9jjcrEa+9fRL51zn+InXz3tdnZm/4cVpt5PPfHUXew+/zNipNiPDQ6xYNJ97b1vNkGGb1DdKHgM/un4F/+fLv8v4/MV89CN3sXbZKH/+7A8u6rtaEYwMD3HyVPuNZSPDQwxFeLyWJKkPlWwn9Eq7nWzff4znXnqF5QsvY+2y0RmvbW5+y8JZLF01DIkknZft+4+x9/DLb1zAnTzVZu/hl9m+/xg3XHtFxaWTVIWhoWDkpb3w0l5uuPZfdfVda5eNsmLRfHZ8/0VoDTPnkmFWLJrP2mWjhUorSZJmU8l2Qi94E/zMHG4m6bw899IrjE25ww8wdqrNcy+9UlGJJA2SoaHg3ttWM3/nV5j77Df5lVtWNr6RJkmSpmu3k237jvKlbQfYtu8o7S6mv5h6EzyZfhO8yexJJOm8LF942RmHgixfeFmFpZI0SOp+x1GSJFWndM+fmW6CN3mkhD2JpBopmYyXNjkUhFMTk8/N6RyUHQoiSZIkqddK9/yZvAk+lTfB7Ukk1Ubdx8RODgX58Mc+zvj8xdzdmXyuDmWTJDXThU44KknqX6V7/jgf4pkZEkk10Q8TQ5ceCmLjXpqZdUQ6u7rfXJEklVV6+gtvgp+ZIZFUE70YE1vnC0wb99LMrCPSzPrh5ookqZxe9PxxPsQfZUgk1UTpZLzuF5g27rtX5xBQ3bOOSDNzwlFJTde0tqA9f2aHIZFUE6WT8bpfYNq4707dQ8CmKtlYs45IM+uHp2427QJO0uxpalvQnj+9Z0gk1UTpZLzuF5j90Livs7qHgE1UurFmHdHFaFIoUfcJR5t6ASdpdtgWVK8MnXsVSbNlMhmfu+/PuOHaK7pqRNb9kY6Tdm3WrwAADzdJREFUjXtOjUG2mdNpPNelcV93M4WAqkbpx7JaR3ShJkOJ+x7fw6NPHeC+x/fwma/uot3OqovWE5M3V+bv/Apzn/0mv3LLyloFMKWPCZI0lW1B9YohkTSg6n6BWffGfd3VPQRsotKNNeuILlQvQol2O9m27yhf2naAbfuO1i5wKnlzpTQv4CT1km1B9YohkTSg+uECs86N+7qrewjYRL1orFlHdCFKhxJN65lUmhdwGkR1D46bxLagesWQSBpgXmAOrn4IAZvGxpqqVjqUcLhUdzwmaNAYHNdLL9qChoCCQiFRRGyIiN0RsTci7jnD53Mi4uHO509ExPIS25WkJjMErBeDO1WtdCjhcKnueEzonhes9dLE4Lju+2DJtqAhoCZ1/XSziGgBnwPeCRwAnoyITZm5c8pqG4GjmbkiIu4APgv8w263rXpp0hNVJJ2fph0XfCyrqlT6KZlNfMJe6WOWx4SL16unw9X9vFTn8tX9ybml9WIfrPPf16elaVLXIRFwI7A3M58BiIgvArcDU0Oi24F/03n9KPCfIyIy01hyQDT1Ma91PtBLF6PkPt3U44JUpZKhRN0fMV+ax6x66cUFa93/xnUPJZoWHJfeB+u+/zUtBNTZRbc5TUS8D9iQmR/qvP8gcFNm3j1lne921jnQef90Z50Xz/a9V167Ot9574Ndla0Otv/FdgDW/uTac657/LXXz7nOnp3fBWDlmp/ormCF/fC1Uzx/7ARTd6cIuGZ0LpdfWiKLLKfUb5iZfP8HJzjx+jiZE//euZe0+PEr5xJx8Qf60n/jpn1fSZnJyyfHee31cS69pMX8Oa2u/ra9UPL3K71P99NxoaS615GmfV8TlT4ufG/vM9AaYcmSqwf6ONirY1ad9+k6n+eO/PAkL7489iPLr5o/wpsun3NR31n381Lp8pU+r09+36snXweCGIqu275N2gfrfoype/l6+Z0X8n0LLr2kyDar9shHfuqpzFx3ps9qFRJFxF3AXQDzr37L3/65X3+oq7L1m/MJiUorVcF6cSKfVNfGVd0bGk1VOgQs2RAqWb5eKL1P9+K4UPqCtR8ugNWdfgix6nxc6IW6/nt72ZYpqSnnuV60s3r1N65re7oXv+FkqHPy9XHmdBnqNG0frPsxplc3wAdNE0KiEleyzwPLprxf2ll2pnUORMQw8GPAS6d/UWbeD9wPsG7dunz4wzcXKF7/+NbTP/KT9NxHf//XAPjkr27q6nu27TvKfY/vmdb9dM7wEL/8U9d13T2xVBlL+9K2Azz61IHpCxNufvNC3nvD0moK1XDtdvLhrz3A+PzFvOtv/WxXXaon92liYn7/TBhvJz//1mu62qfruj9D+X269HFhspt2+9JRaA1z5Icn+bG5F99Nu/T3qZ5K17le1OE6Hxd6oa7/3l62ZUoq3Xar63muF0NzevU3rmt7uu5t1V7sgyXbgqX3wX44xjiVxrnd/JaFVRehiEc+cvbPSoRETwIrI+I6JsKgO4BfPG2dTcCdwLeA9wGPOx/RYJmct+D0g+igzlsAzRuXXXeTJ/KX17wHWsPc9/ierk7kvRiX3W4nYwtXMD5/Mdv2Ha3dibf0Pl36uDA5NwDDI0D3cwOU/j5J/a1pbZm6zz8yORF7yQvWXvyNS57bS5ev7m3V0vtg6bZg6X2wH44xQ0PBDddeUYtjgKrTdUiUmaci4m7ga0ALeDAzd0TEp4CtmbkJeAB4KCL2Aj9gIkjSAOnFibzu+uFA3ySlL/hLN6xKN1x6ofQ+Xfq4ULoxWfcLJEmzq2ltmboHCFD+grX039hQojul98Fe3PwpuQ827Rij/lVk4pTMfAx47LRln5zy+jXgF0psS/XVtOTZA329lL7gr3svmF7oxT5d8rhQujHZDxdIqpe69wZU95rUlql7gNArJf/GhhLdKb0P9sPNnyYdY9S/nF1X6oIH+voofcFf914wvVLnfbp0Y7KpF0i6OP3QG1C6EHUPEPpBP5zb63xeL70PevNHKsOQSNJA6MUFf517wTRR6cakF0i6EL3qDWjvJFWpdIDQtP3Zc3v3Su6D3vyRyjAkkjQQ6n7Bb8OljF7MT1HXO6yql15NZm/vJA2KJu7Pntvrpe5tQalfGBJJGhh1vuC34SL1t170GOiHucqk89XE/dlze/3UuS0o9QtDIkmaJTZcpNlV50dTQ3/MZyKdr6buz57bJQ0aQyJJkjRw6v5oanA+Ew0W92dJGgxDVRdAkiSptGlDX2Jo2tCXizXZY+C9Nyzlhmuv6HpIyWTvpDnDQwQwx/lM1MfcnyVpMNiTSJIkDZx+GPrifCYaJO7PkjQYDIkkSdLA6ZehL85nokHi/ixJ/c/hZpIkaeA49EWSJOnC2ZNIkiQNHIe+SJIkXThDIkmSNJAc+iJJknRhHG6mWmu3k7GFKzhx7U+zbd9R2u2sukiSJOkieV7vjr+fJKnX7Emk2mq3k898dRcvr3kPtIa57/E9rFg0n3tvW+1wAUmS+ozn9e74+0mSZoMhUY3c/JaFs77NBXMvqWzb57J51yGeffEVGB4B4OSpNs+++AqvnRrn1tWLKy6dJEm6EJ7Xu+PvJ0maDQ43U23tOHicE2Pj05adGBtn58HjFZVIkiRdLM/r3fH3kyTNBkMi1db1SxYwd6Q1bdnckRZrliyoqESSJOlieV7vjr+fJGk2GBKpttavWsTaZaPMG2kRwLyRFmuXjbJ+1aKqiyZJki6Q5/Xu+PtJkmaDcxKptlpDwUMbb2LL7sPsPHicNUsWsH7VIlpOzihJUt/xvN4dfz9J0mzoKiSKiCuBh4HlwHPA+zPz6GnrrAV+F1gAjAOfzsyHu9mumqM1FNy6erETMkqSNAA8r3fH30+S1GvdDje7B9icmSuBzZ33p3sV+MeZeT2wAfjtiBjtcruSJEmSJEkqqNuQ6HbgC53XXwDec/oKmfm9zNzTeX0QOAxc1eV2JUmSJEmSVFC3IdHizHyh8/qvgBn7vkbEjcAI8PRZPr8rIrZGxNYjR450WTRJkiRJkiSdr3POSRQR/wv4G2f46BNT32RmRkTO8D1XAw8Bd2Zm+0zrZOb9wP0A69atO+t3SZIkSZIkqaxzhkSZ+Y6zfRYRhyLi6sx8oRMCHT7LeguAPwU+kZnfvujSSpIkSZIkqSe6HW62Cbiz8/pO4I9PXyEiRoAvA/8tMx/tcnuSJEmSJEnqgW5Dot8A3hkRe4B3dN4TEesi4vOddd4P/B3glyNie+e/tV1uV5IkSZIkSQWdc7jZTDLzJeDWMyzfCnyo8/r3gN/rZjuSJEmSJEnqrW57EkmSJEmSJGkAGBJJkiRJkiTJkEiSJEmSJEmGRJIkSZIkScKQSJIkSZIkSRgSSZIkSZIkCUMiSZIkSZIkYUgkSZIkSZIkDIkabbydvDr6Zo5dczObdx1ivJ1VF0mSJEmSJFVkuOoCqBrj7eSDDzzBkZU/Tw4N80//4DusXTbKQxtvojUUVRdPkiRJkiTNMnsSNdSW3YfZvv8Y2RqBGOLVsXG27z/Glt2Hqy6aJEmSJEmqgCFRQ+04eJwTY+PTlp0YG2fnweMVlUiSJEmSJFXJkKihrl+ygLkjrWnL5o60WLNkQUUlkiRJkiRJVTIkaqj1qxaxdtko80ZaBDBvpMXaZaOsX7Wo6qJJkiRJkqQKOHF1Q7WGgoc23sSW3YfZefA4a5YsYP2qRU5aLUmSJElSQxkSNVhrKLh19WJuXb246qJIkiRJkqSKOdxMkiRJkiRJhkSSJEmSJEkyJJIkSZIkSRKGRJIkSZIkSQIiM6suwxlFxBFgX9XlKORNwItVF0KqMeuINDPriDQz64g0M+uINLOm1ZFrM/OqM31Q25BokETE1sxcV3U5pLqyjkgzs45IM7OOSDOzjkgzs478NYebSZIkSZIkyZBIkiRJkiRJhkSz5f6qCyDVnHVEmpl1RJqZdUSamXVEmpl1pMM5iSRJkiRJkmRPIkmSJEmSJBkS9VxEbIiI3RGxNyLuqbo8UtUi4sGIOBwR352y7MqI+HpE7On8/4oqyyhVJSKWRcQ3ImJnROyIiI91lltHJCAiLo2IP4+Iv+jUkX/bWX5dRDzRaW89HBEjVZdVqlJEtCLiOxHxJ5331hGpIyKei4i/jIjtEbG1s8y2VochUQ9FRAv4HHAbsAb4QESsqbZUUuX+K7DhtGX3AJszcyWwufNeaqJTwMczcw3wduCjnfOGdUSacBK4JTN/ElgLbIiItwOfBf5jZq4AjgIbKyyjVAcfA3ZNeW8dkab7e5m5dspj721rdRgS9daNwN7MfCYzx4AvArdXXCapUpn5v4EfnLb4duALnddfAN4zq4WSaiIzX8jMbZ3XP2SigX8N1hEJgJzwcuftJZ3/ErgFeLSz3DqiRouIpcDfBz7feR9YR6Rzsa3VYUjUW9cA+6e8P9BZJmm6xZn5Quf1XwGLqyyMVAcRsRx4G/AE1hHpDZ1hNNuBw8DXgaeBY5l5qrOK7S013W8Dvwq0O+8XYh2Rpkrgf0bEUxFxV2eZba2O4aoLIElTZWZGhI9dVKNFxHzgj4B/lpnHJ24CT7COqOkycxxYGxGjwJeBv1lxkaTaiIh3AYcz86mIWF91eaSa+pnMfD4iFgFfj4j/O/XDpre17EnUW88Dy6a8X9pZJmm6QxFxNUDn/4crLo9UmYi4hImA6L9n5pc6i60j0mky8xjwDeBmYDQiJm9+2t5Sk/008O6IeI6JqS5uAX4H64j0hsx8vvP/w0zcbLgR21pvMCTqrSeBlZ2nCYwAdwCbKi6TVEebgDs7r+8E/rjCskiV6cwb8QCwKzN/a8pH1hEJiIirOj2IiIi5wDuZmLvrG8D7OqtZR9RYmflrmbk0M5czce3xeGb+I6wjEgARcVlEXD75GvhZ4LvY1npDZDa2F9WsiIifY2JccAt4MDM/XXGRpEpFxB8A64E3AYeAXwe+AjwC/DiwD3h/Zp4+ubU08CLiZ4BvAn/JX88lcS8T8xJZR9R4EfFWJiYUbTFxs/ORzPxURLyZiV4TVwLfAX4pM09WV1Kpep3hZv8yM99lHZEmdOrClztvh4Hfz8xPR8RCbGsBhkSSJEmSJEnC4WaSJEmSJEnCkEiSJEmSJEkYEkmSJEmSJAlDIkmSJEmSJGFIJEmSJEmSJAyJJEmSJEmShCGRJEmSJEmSMCSSJEmSJEkS8P8BaVpgCRnt8qoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q2GBj_USvdYS"
      },
      "source": [
        "## Fitting the ARIMA model using (p,d,q) = (3,1,1):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QoqsFHJD-3-3",
        "outputId": "bff13769-9045-4b12-a8f6-f0de273c7be6"
      },
      "source": [
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "#prepare data\n",
        "X = series.values\n",
        "X = X.astype('float32')\n",
        "train_size = int(len(X) * 0.50)\n",
        "train, test = X[0:train_size], X[train_size:]\n",
        "# walk-forward validation\n",
        "history = [x for x in train]\n",
        "predictions = list()\n",
        "for i in range(len(test)): \n",
        "  # predict\n",
        "  model = ARIMA(history, order=(3,1,1))\n",
        "  model_fit = model.fit(disp=0)\n",
        "  yhat = model_fit.forecast()[0]\n",
        "  predictions.append(yhat)\n",
        "  # observation\n",
        "  obs = test[i]\n",
        "  history.append(obs)\n",
        "  print('>Predicted=%.3f, Expected=%.3f' % (yhat, obs))\n",
        "# report performance\n",
        "rmse = sqrt(mean_squared_error(test, predictions))\n",
        "print('RMSE: %.3f' % rmse)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ">Predicted=1158.246, Expected=1038.000\n",
            ">Predicted=1277.257, Expected=1143.000\n",
            ">Predicted=1323.930, Expected=1123.000\n",
            ">Predicted=1249.149, Expected=1269.000\n",
            ">Predicted=1414.393, Expected=1921.000\n",
            ">Predicted=2088.913, Expected=1910.000\n",
            ">Predicted=1818.837, Expected=1964.000\n",
            ">Predicted=1861.487, Expected=2033.000\n",
            ">Predicted=1926.890, Expected=1518.000\n",
            ">Predicted=1336.610, Expected=1410.000\n",
            ">Predicted=1410.541, Expected=1431.000\n",
            ">Predicted=1503.028, Expected=1850.000\n",
            ">Predicted=1962.219, Expected=2756.000\n",
            ">Predicted=2823.026, Expected=4530.000\n",
            ">Predicted=4693.627, Expected=5217.000\n",
            ">Predicted=4775.367, Expected=4676.000\n",
            ">Predicted=3668.178, Expected=4705.000\n",
            ">Predicted=4253.429, Expected=2609.000\n",
            ">Predicted=1388.078, Expected=1477.000\n",
            ">Predicted=767.527, Expected=1300.000\n",
            ">Predicted=1345.196, Expected=1288.000\n",
            ">Predicted=1521.360, Expected=1320.000\n",
            ">Predicted=1578.730, Expected=1640.000\n",
            ">Predicted=1949.191, Expected=2288.000\n",
            ">Predicted=2604.936, Expected=2981.000\n",
            ">Predicted=3164.817, Expected=2691.000\n",
            ">Predicted=2478.677, Expected=2404.000\n",
            ">Predicted=2178.378, Expected=2338.000\n",
            ">Predicted=2249.626, Expected=2420.000\n",
            ">Predicted=2410.231, Expected=2404.000\n",
            ">Predicted=2371.814, Expected=2142.000\n",
            ">Predicted=2061.326, Expected=2242.000\n",
            ">Predicted=2288.792, Expected=2113.000\n",
            ">Predicted=2127.096, Expected=1882.000\n",
            ">Predicted=1892.873, Expected=2017.000\n",
            ">Predicted=2158.721, Expected=2519.000\n",
            ">Predicted=2732.946, Expected=2671.000\n",
            ">Predicted=2707.893, Expected=4920.000\n",
            ">Predicted=5277.335, Expected=5318.000\n",
            ">Predicted=4977.533, Expected=3800.000\n",
            ">Predicted=2646.762, Expected=3432.000\n",
            ">Predicted=2901.476, Expected=2367.000\n",
            ">Predicted=1918.025, Expected=2086.000\n",
            ">Predicted=2022.617, Expected=1763.000\n",
            ">Predicted=1875.003, Expected=1333.000\n",
            ">Predicted=1519.425, Expected=1343.000\n",
            ">Predicted=1709.996, Expected=1387.000\n",
            ">Predicted=1797.891, Expected=1578.000\n",
            ">Predicted=1992.043, Expected=1583.000\n",
            ">Predicted=1914.405, Expected=1525.000\n",
            ">Predicted=1812.376, Expected=1328.000\n",
            ">Predicted=1589.284, Expected=1245.000\n",
            ">Predicted=1556.017, Expected=1371.000\n",
            ">Predicted=1744.376, Expected=1468.000\n",
            ">Predicted=1811.441, Expected=1278.000\n",
            ">Predicted=1525.181, Expected=1050.000\n",
            ">Predicted=1300.656, Expected=1096.000\n",
            ">Predicted=1445.413, Expected=1195.000\n",
            ">Predicted=1552.614, Expected=1018.000\n",
            ">Predicted=1281.511, Expected=1190.000\n",
            ">Predicted=1543.275, Expected=1152.000\n",
            ">Predicted=1431.191, Expected=2336.000\n",
            ">Predicted=2865.687, Expected=1755.000\n",
            ">Predicted=1779.215, Expected=2886.000\n",
            ">Predicted=3096.800, Expected=4110.000\n",
            ">Predicted=4311.869, Expected=4214.000\n",
            ">Predicted=3954.753, Expected=4095.000\n",
            ">Predicted=3665.818, Expected=2371.000\n",
            ">Predicted=1688.885, Expected=1481.000\n",
            ">Predicted=1180.349, Expected=1173.000\n",
            ">Predicted=1307.342, Expected=1179.000\n",
            ">Predicted=1513.842, Expected=1410.000\n",
            ">Predicted=1812.000, Expected=1929.000\n",
            ">Predicted=2339.547, Expected=1781.000\n",
            ">Predicted=1979.516, Expected=1533.000\n",
            ">Predicted=1667.330, Expected=1791.000\n",
            ">Predicted=2055.937, Expected=1817.000\n",
            ">Predicted=2034.399, Expected=1533.000\n",
            ">Predicted=1663.530, Expected=1263.000\n",
            ">Predicted=1428.451, Expected=959.000\n",
            ">Predicted=1178.537, Expected=979.000\n",
            ">Predicted=1317.033, Expected=1211.000\n",
            ">Predicted=1607.278, Expected=1263.000\n",
            ">Predicted=1583.608, Expected=1762.000\n",
            ">Predicted=2127.391, Expected=1941.000\n",
            ">Predicted=2173.678, Expected=2252.000\n",
            ">Predicted=2430.939, Expected=3675.000\n",
            ">Predicted=3990.024, Expected=3567.000\n",
            ">Predicted=3397.626, Expected=6415.000\n",
            ">Predicted=6464.825, Expected=9876.000\n",
            ">Predicted=11132.033, Expected=4936.000\n",
            ">Predicted=3473.611, Expected=2950.000\n",
            ">Predicted=4377.908, Expected=2100.000\n",
            ">Predicted=1963.980, Expected=1100.000\n",
            ">Predicted=1215.644, Expected=794.000\n",
            ">Predicted=1221.298, Expected=961.000\n",
            ">Predicted=1524.468, Expected=916.000\n",
            ">Predicted=1422.826, Expected=1155.000\n",
            ">Predicted=1669.750, Expected=1950.000\n",
            ">Predicted=2441.360, Expected=3420.000\n",
            ">Predicted=3762.544, Expected=3200.000\n",
            ">Predicted=3044.022, Expected=3060.000\n",
            "RMSE: 969.031\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKBMJsiPvobM"
      },
      "source": [
        "## Hyper parameter tuning to find the best (p,d,q) values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V3LieMLoAwT9",
        "outputId": "ddb69ddb-11c7-4f1b-ccf6-6f8336f5a6ae"
      },
      "source": [
        "import warnings\n",
        "# evaluate an ARIMA model for a given order (p,d,q) and return RMSE\n",
        "def evaluate_arima_model(X, arima_order):\n",
        "  # prepare training dataset\n",
        "  X = X.astype('float32')\n",
        "  train_size = int(len(X) * 0.50)\n",
        "  train, test = X[0:train_size], X[train_size:]\n",
        "  history = [x for x in train]\n",
        "  # make predictions\n",
        "  predictions = list()\n",
        "  for t in range(len(test)):\n",
        "    model = ARIMA(history, order=arima_order)\n",
        "    model_fit = model.fit(disp=0)\n",
        "    yhat = model_fit.forecast()[0]\n",
        "    predictions.append(yhat)\n",
        "    history.append(test[t])\n",
        "  # calculate out of sample error\n",
        "  rmse = sqrt(mean_squared_error(test, predictions))\n",
        "  return rmse\n",
        "\n",
        "# evaluate combinations of p, d and q values for an ARIMA model\n",
        "def evaluate_models(dataset, p_values, d_values, q_values):\n",
        "  dataset = dataset.astype('float32')\n",
        "  best_score, best_cfg = float(\"inf\"), None\n",
        "  for p in p_values:\n",
        "    for d in d_values:\n",
        "      for q in q_values:\n",
        "        order = (p,d,q)\n",
        "        try:\n",
        "          rmse = evaluate_arima_model(dataset, order)\n",
        "          if rmse < best_score:\n",
        "            best_score, best_cfg = rmse, order\n",
        "          print('ARIMA%s RMSE=%.3f' % (order,rmse))\n",
        "        except:\n",
        "          continue\n",
        "  print('Best ARIMA%s RMSE=%.3f' % (best_cfg, best_score))\n",
        "\n",
        "# evaluate parameters\n",
        "p_values = range(0,5)\n",
        "d_values = range(0,2)\n",
        "q_values = range(0,3)\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "evaluate_models(series.values, p_values, d_values, q_values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ARIMA(0, 0, 0) RMSE=1643.207\n",
            "ARIMA(0, 0, 1) RMSE=1051.565\n",
            "ARIMA(0, 1, 0) RMSE=927.432\n",
            "ARIMA(0, 1, 1) RMSE=921.728\n",
            "ARIMA(0, 1, 2) RMSE=983.365\n",
            "ARIMA(1, 0, 0) RMSE=927.817\n",
            "ARIMA(1, 0, 1) RMSE=917.461\n",
            "ARIMA(1, 0, 2) RMSE=955.266\n",
            "ARIMA(1, 1, 0) RMSE=958.505\n",
            "ARIMA(1, 1, 1) RMSE=979.788\n",
            "ARIMA(2, 0, 0) RMSE=923.024\n",
            "ARIMA(2, 0, 1) RMSE=958.208\n",
            "ARIMA(2, 1, 0) RMSE=969.962\n",
            "ARIMA(3, 0, 0) RMSE=937.448\n",
            "ARIMA(3, 1, 0) RMSE=990.181\n",
            "ARIMA(3, 1, 1) RMSE=969.031\n",
            "ARIMA(4, 0, 0) RMSE=938.818\n",
            "ARIMA(4, 0, 1) RMSE=913.528\n",
            "ARIMA(4, 1, 0) RMSE=956.340\n",
            "ARIMA(4, 1, 1) RMSE=936.871\n",
            "Best ARIMA(4, 0, 1) RMSE=913.528\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1Ken8nTPgZh"
      },
      "source": [
        "ARIMA(4,0,1) gives us the better RMSE value. So, let's take that for further steps."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EfUkoarZv-_M"
      },
      "source": [
        "## Train the ARIMA model using (4,0,1) and analysing the residual errors that we got:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "szzhkZ8dCC_D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "cec45f9a-d928-4e1c-adb1-7926ae3fb7fe"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "# prepare data\n",
        "X = series.values\n",
        "X = X.astype('float32')\n",
        "train_size = int(len(X) * 0.50)\n",
        "train, test = X[0:train_size], X[train_size:]\n",
        "# walk-forward validation\n",
        "history = [x for x in train]\n",
        "predictions = list()\n",
        "for i in range(len(test)):\n",
        "  # predict\n",
        "  model = ARIMA(history, order=(4,0,1))\n",
        "  model_fit = model.fit(disp=0)\n",
        "  yhat = model_fit.forecast()[0]\n",
        "  predictions.append(yhat)\n",
        "  # observation\n",
        "  obs = test[i]\n",
        "  history.append(obs)\n",
        "# errors\n",
        "residuals = [test[i]-predictions[i] for i in range(len(test))]\n",
        "residuals = pd.DataFrame(residuals)\n",
        "\n",
        "plt.figure(figsize=(20,8))\n",
        "plt.subplot(121)\n",
        "residuals.hist(ax=plt.gca())\n",
        "plt.subplot(122)\n",
        "residuals.plot(kind='kde', ax=plt.gca())\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3rF-9cHIwLsm"
      },
      "source": [
        "The mean of the residual errors stays at \"0\". So, there is no necessary in addition or subraction of bias."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMjLTRnOwYOj"
      },
      "source": [
        "## ACF and PACF of residual errors:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "E_S524dXbHFU",
        "outputId": "702aadca-089c-43b9-f309-8093f70bd275"
      },
      "source": [
        "plt.figure(figsize=(20,8))\n",
        "plt.subplot(121)\n",
        "plot_acf(residuals, lags=25, ax=plt.gca())\n",
        "plt.subplot(122)\n",
        "plot_pacf(residuals, lags=25, ax=plt.gca())\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZgSZEffwd9G"
      },
      "source": [
        "## Transforming the dataset using boxcox and finding the best lambda value:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "id": "YQfBi2uTbNmo",
        "outputId": "c5e1defa-872f-4649-ed09-56c8da38da5b"
      },
      "source": [
        "from scipy.stats import boxcox\n",
        "from statsmodels.graphics.gofplots import qqplot\n",
        "X = series.values\n",
        "transformed, lam = boxcox(X)\n",
        "print('Lambda: %f' % lam)\n",
        "plt.figure(1)\n",
        "plt.figure(figsize=(20,8))\n",
        "# line plot\n",
        "plt.subplot(311)\n",
        "plt.plot(transformed)\n",
        "# histogram\n",
        "plt.subplot(312)\n",
        "plt.hist(transformed)\n",
        "# q-q plot\n",
        "plt.subplot(313)\n",
        "qqplot(transformed, line='r', ax=plt.gca())\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Lambda: -0.211850\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "viplmur8wnvt"
      },
      "source": [
        "Best lambda value = -0.211850"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FPdACrdlwtUm"
      },
      "source": [
        "## Creating a function to inverse the tranformation that was made:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpy0noPfcRuz"
      },
      "source": [
        "from math import log\n",
        "from math import exp\n",
        "# invert box-cox transform\n",
        "def boxcox_inverse(value, lam):\n",
        "  if lam == 0:\n",
        "    return exp(value)\n",
        "  return exp(log(lam * value + 1) / lam)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uneLuoaRw1Lr"
      },
      "source": [
        "## Training the ARIMA model with the transformed data as an input:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7zdY5RhDeCgG",
        "outputId": "32f8c242-299b-414c-d247-7e72c06ff71c"
      },
      "source": [
        "X = X.astype('float32')\n",
        "train_size = int(len(X) * 0.50)\n",
        "train, test = X[0:train_size], X[train_size:]\n",
        "# walk-forward validation\n",
        "history = [x for x in train]\n",
        "predictions = list()\n",
        "for i in range(len(test)):\n",
        "  # transform\n",
        "  transformed, lam = boxcox(history)\n",
        "  if lam < -5:\n",
        "    transformed, lam = history, 1\n",
        "  # predict\n",
        "  model = ARIMA(transformed, order=(4,0,1))\n",
        "  model_fit = model.fit(disp=0)\n",
        "  yhat = model_fit.forecast()[0]\n",
        "  # invert transformed prediction\n",
        "  yhat = boxcox_inverse(yhat, lam)\n",
        "  predictions.append(yhat)\n",
        "  # observation\n",
        "  obs = test[i]\n",
        "  history.append(obs)\n",
        "  print('>Predicted=%.3f, Expected=%.3f' % (yhat, obs))\n",
        "# report performance\n",
        "rmse = sqrt(mean_squared_error(test, predictions))\n",
        "print('RMSE: %.3f' % rmse)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ">Predicted=955.786, Expected=1038.000\n",
            ">Predicted=1094.019, Expected=1143.000\n",
            ">Predicted=1180.369, Expected=1123.000\n",
            ">Predicted=1122.297, Expected=1269.000\n",
            ">Predicted=1359.757, Expected=1921.000\n",
            ">Predicted=2137.157, Expected=1910.000\n",
            ">Predicted=1812.540, Expected=1964.000\n",
            ">Predicted=2017.631, Expected=2033.000\n",
            ">Predicted=2113.386, Expected=1518.000\n",
            ">Predicted=1379.118, Expected=1410.000\n",
            ">Predicted=1498.922, Expected=1431.000\n",
            ">Predicted=1495.637, Expected=1850.000\n",
            ">Predicted=2017.157, Expected=2756.000\n",
            ">Predicted=3023.416, Expected=4530.000\n",
            ">Predicted=4830.463, Expected=5217.000\n",
            ">Predicted=4933.085, Expected=4676.000\n",
            ">Predicted=3778.148, Expected=4705.000\n",
            ">Predicted=4331.685, Expected=2609.000\n",
            ">Predicted=1241.595, Expected=1477.000\n",
            ">Predicted=850.541, Expected=1300.000\n",
            ">Predicted=926.995, Expected=1288.000\n",
            ">Predicted=1385.111, Expected=1320.000\n",
            ">Predicted=1193.582, Expected=1640.000\n",
            ">Predicted=1842.810, Expected=2288.000\n",
            ">Predicted=2291.230, Expected=2981.000\n",
            ">Predicted=3149.742, Expected=2691.000\n",
            ">Predicted=2175.074, Expected=2404.000\n",
            ">Predicted=2227.474, Expected=2338.000\n",
            ">Predicted=1958.854, Expected=2420.000\n",
            ">Predicted=2410.146, Expected=2404.000\n",
            ">Predicted=2069.039, Expected=2142.000\n",
            ">Predicted=2016.246, Expected=2242.000\n",
            ">Predicted=2013.972, Expected=2113.000\n",
            ">Predicted=2023.648, Expected=1882.000\n",
            ">Predicted=1587.680, Expected=2017.000\n",
            ">Predicted=2070.728, Expected=2519.000\n",
            ">Predicted=2465.367, Expected=2671.000\n",
            ">Predicted=2615.535, Expected=4920.000\n",
            ">Predicted=5507.966, Expected=5318.000\n",
            ">Predicted=4873.729, Expected=3800.000\n",
            ">Predicted=2854.580, Expected=3432.000\n",
            ">Predicted=2993.776, Expected=2367.000\n",
            ">Predicted=1674.082, Expected=2086.000\n",
            ">Predicted=1977.908, Expected=1763.000\n",
            ">Predicted=1402.581, Expected=1333.000\n",
            ">Predicted=1313.810, Expected=1343.000\n",
            ">Predicted=1219.164, Expected=1387.000\n",
            ">Predicted=1525.375, Expected=1578.000\n",
            ">Predicted=1533.600, Expected=1583.000\n",
            ">Predicted=1654.895, Expected=1525.000\n",
            ">Predicted=1402.506, Expected=1328.000\n",
            ">Predicted=1335.875, Expected=1245.000\n",
            ">Predicted=1167.939, Expected=1371.000\n",
            ">Predicted=1504.573, Expected=1468.000\n",
            ">Predicted=1426.597, Expected=1278.000\n",
            ">Predicted=1288.096, Expected=1050.000\n",
            ">Predicted=949.848, Expected=1096.000\n",
            ">Predicted=1234.358, Expected=1195.000\n",
            ">Predicted=1195.844, Expected=1018.000\n",
            ">Predicted=1068.154, Expected=1190.000\n",
            ">Predicted=1259.921, Expected=1152.000\n",
            ">Predicted=1188.485, Expected=2336.000\n",
            ">Predicted=2758.610, Expected=1755.000\n",
            ">Predicted=1319.454, Expected=2886.000\n",
            ">Predicted=3276.371, Expected=4110.000\n",
            ">Predicted=3832.273, Expected=4214.000\n",
            ">Predicted=4255.919, Expected=4095.000\n",
            ">Predicted=3363.621, Expected=2371.000\n",
            ">Predicted=1771.809, Expected=1481.000\n",
            ">Predicted=816.299, Expected=1173.000\n",
            ">Predicted=1156.879, Expected=1179.000\n",
            ">Predicted=1076.071, Expected=1410.000\n",
            ">Predicted=1648.805, Expected=1929.000\n",
            ">Predicted=1954.792, Expected=1781.000\n",
            ">Predicted=1791.887, Expected=1533.000\n",
            ">Predicted=1352.313, Expected=1791.000\n",
            ">Predicted=1915.207, Expected=1817.000\n",
            ">Predicted=1671.674, Expected=1533.000\n",
            ">Predicted=1524.457, Expected=1263.000\n",
            ">Predicted=1076.022, Expected=959.000\n",
            ">Predicted=998.236, Expected=979.000\n",
            ">Predicted=976.839, Expected=1211.000\n",
            ">Predicted=1424.928, Expected=1263.000\n",
            ">Predicted=1247.891, Expected=1762.000\n",
            ">Predicted=2036.431, Expected=1941.000\n",
            ">Predicted=1823.795, Expected=2252.000\n",
            ">Predicted=2416.590, Expected=3675.000\n",
            ">Predicted=3792.294, Expected=3567.000\n",
            ">Predicted=3282.426, Expected=6415.000\n",
            ">Predicted=6878.523, Expected=9876.000\n",
            ">Predicted=10631.380, Expected=4936.000\n",
            ">Predicted=2696.694, Expected=2950.000\n",
            ">Predicted=2846.064, Expected=2100.000\n",
            ">Predicted=339.767, Expected=1100.000\n",
            ">Predicted=1655.861, Expected=794.000\n",
            ">Predicted=151.661, Expected=961.000\n",
            ">Predicted=1651.101, Expected=916.000\n",
            ">Predicted=586.707, Expected=1155.000\n",
            ">Predicted=1736.470, Expected=1950.000\n",
            ">Predicted=1768.049, Expected=3420.000\n",
            ">Predicted=3618.018, Expected=3200.000\n",
            ">Predicted=2746.338, Expected=3060.000\n",
            "RMSE: 913.386\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "icoQ4MX5w9W6"
      },
      "source": [
        "## Saving the model and lambda value for further use:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZprBx_GexVt"
      },
      "source": [
        "X = series.values\n",
        "X = X.astype('float32')\n",
        "for i in range(len(X)):\n",
        "  # transform\n",
        "  transformed, lam = boxcox(X)\n",
        "  if lam < -5:\n",
        "    transformed, lam = X, 1\n",
        "# fit model\n",
        "model = ARIMA(transformed, order=(4,0,1))\n",
        "model_fit = model.fit(disp=0)\n",
        "# save model\n",
        "model_fit.save('model.pkl')\n",
        "np.save('model_lambda.npy', [lam])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R86qAqz6xEsQ"
      },
      "source": [
        "## Loading the saved model and forecasted the next output:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eXoj_QLlgYXV",
        "outputId": "9403578b-ffcb-4f86-92bb-9c3f8c274c2d"
      },
      "source": [
        "from statsmodels.tsa.arima_model import ARIMAResults\n",
        "\n",
        "model_fit = ARIMAResults.load('model.pkl')\n",
        "lam = np.load('model_lambda.npy')\n",
        "yhat = model_fit.forecast()[0]\n",
        "yhat = boxcox_inverse(yhat, lam)\n",
        "print('Predicted: %.3f' % yhat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted: 2957.058\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C5FIQfguxLdk"
      },
      "source": [
        "## Finally, checking the performance of the model by predicting the outcomes of validation data that was set aside initially:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VuYO-Z0cj8QT",
        "outputId": "36d3ae0a-1bce-4013-f782-04efe0e0b397"
      },
      "source": [
        "dataset = pd.read_csv('onion_dataset_final.csv', header=None, index_col=0, parse_dates=True, squeeze=True)\n",
        "X = dataset.values.astype('float32')\n",
        "history = [x for x in X]\n",
        "validation = pd.read_csv('onion_validation.csv', header=None, index_col=0, parse_dates=True,\n",
        "squeeze=True)\n",
        "y = validation.values.astype('float32')\n",
        "# load model\n",
        "model_fit = ARIMAResults.load('model.pkl')\n",
        "lam = np.load('model_lambda.npy')\n",
        "# make first prediction\n",
        "predictions = list()\n",
        "yhat = model_fit.forecast()[0]\n",
        "yhat = boxcox_inverse(yhat, lam)\n",
        "predictions.append(yhat)\n",
        "history.append(y[0])\n",
        "print('>Predicted=%.3f, Expected=%.3f' % (yhat, y[0]))\n",
        "# rolling forecasts\n",
        "for i in range(1, len(y)):\n",
        "  # transform\n",
        "  transformed, lam = boxcox(history)\n",
        "  if lam < -5:\n",
        "    transformed, lam = history, 1\n",
        "  # predict\n",
        "  model = ARIMA(transformed, order=(4,0,1))\n",
        "  model_fit = model.fit(disp=0)\n",
        "  yhat = model_fit.forecast()[0]\n",
        "  # invert transformed prediction\n",
        "  yhat = boxcox_inverse(yhat, lam)\n",
        "  predictions.append(yhat)\n",
        "  # observation\n",
        "  obs = y[i]\n",
        "  history.append(obs)\n",
        "  print('>Predicted=%.3f, Expected=%.3f' % (yhat, obs))\n",
        "# report performance\n",
        "rmse = sqrt(mean_squared_error(y, predictions))\n",
        "print('RMSE: %.3f' % rmse)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ">Predicted=2957.058, Expected=2976.000\n",
            ">Predicted=2432.245, Expected=4211.000\n",
            ">Predicted=4192.713, Expected=3300.000\n",
            ">Predicted=2467.433, Expected=1100.000\n",
            ">Predicted=802.992, Expected=1150.000\n",
            "RMSE: 1091.014\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OER8VzyTxZSs"
      },
      "source": [
        "RMSE value for the validation data is 1091.014"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gDrU3TD4xekz"
      },
      "source": [
        "## Expected vs Predicted:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbIMdK8IqvdE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "outputId": "c5f179b0-4850-4d13-8b3b-07d107d39e13"
      },
      "source": [
        "plt.figure(figsize=(20,8))\n",
        "plt.plot(y)\n",
        "plt.plot(predictions, color='red')\n",
        "plt.legend(['Expected','Predicted'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqU2FoWNsuH-"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}