ARIMA_and_Seasonal_ARIMA.ipynb

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  "metadata": {
    "colab": {
      "name": "ARIMA and Seasonal ARIMA",
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      "mount_file_id": "1bLyLV5jB9vRT8n5H5VM2hbJyVbj2WcPe",
      "authorship_tag": "ABX9TyNMPqSIPegGLd5k5EGT1fng",
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      "name": "python3",
      "display_name": "Python 3"
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      "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/swarnava-96/Time-Series/blob/main/ARIMA_and_Seasonal_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": "qhmYtqfzWeqG"
      },
      "source": [
        "# **Time series Forecasting using ARIMA and Seasonal ARIMA**\n",
        "\n",
        "#### Autoregressive Integrated Moving Averages\n",
        "The general process for ARIMA models is the following:\n",
        "\n",
        "1. Visualize the Time Series Data\n",
        "2. Make the time series data stationary\n",
        "3. Plot the Correlation and AutoCorrelation Charts\n",
        "4. Construct the ARIMA Model or Seasonal ARIMA based on the data\n",
        "5. Use the model to make predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "St5t332eWm38",
        "outputId": "60ba7855-4e66-4ea2-a36d-ed93872619c2"
      },
      "source": [
        "cd \"/content/drive/MyDrive/Datasets\""
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Datasets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4jBFq49kXLt8"
      },
      "source": [
        "### Importing the necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qlbKfLCcWtJo"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zLTYMYeXazN"
      },
      "source": [
        "### Loading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "E27QhoEXXWpa",
        "outputId": "9acc5ee3-2ba1-43ac-9d3a-9cbaddf5a8f7"
      },
      "source": [
        "df = pd.read_csv(\"perrin-freres-monthly-champagne-.csv\")\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
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              "        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>Month</th>\n",
              "      <th>Perrin Freres monthly champagne sales millions ?64-?72</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1964-01</td>\n",
              "      <td>2815.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1964-02</td>\n",
              "      <td>2672.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1964-03</td>\n",
              "      <td>2755.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1964-04</td>\n",
              "      <td>2721.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1964-05</td>\n",
              "      <td>2946.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Month  Perrin Freres monthly champagne sales millions ?64-?72\n",
              "0  1964-01                                             2815.0     \n",
              "1  1964-02                                             2672.0     \n",
              "2  1964-03                                             2755.0     \n",
              "3  1964-04                                             2721.0     \n",
              "4  1964-05                                             2946.0     "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "Pp1kGYcRXjxF",
        "outputId": "8eefbcf5-3f44-4620-9a4d-9674e81a78a5"
      },
      "source": [
        "# Lets see the last five rows \n",
        "\n",
        "df.tail()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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>Month</th>\n",
              "      <th>Perrin Freres monthly champagne sales millions ?64-?72</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>1972-07</td>\n",
              "      <td>4298.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>1972-08</td>\n",
              "      <td>1413.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>1972-09</td>\n",
              "      <td>5877.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>Perrin Freres monthly champagne sales millions...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 Month  Perrin Freres monthly champagne sales millions ?64-?72\n",
              "102                                            1972-07                                             4298.0     \n",
              "103                                            1972-08                                             1413.0     \n",
              "104                                            1972-09                                             5877.0     \n",
              "105                                                NaN                                                NaN     \n",
              "106  Perrin Freres monthly champagne sales millions...                                                NaN     "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "29ab-cbgXuzf"
      },
      "source": [
        "### Data Preprocessing\n",
        "Lets clean up the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "sQWPjntSXpTV",
        "outputId": "e9f617f6-9eed-49c4-949a-1962cb83021c"
      },
      "source": [
        "# Lets rename the columns\n",
        "\n",
        "df.columns = [\"Month\", \"Sales\"]\n",
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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>Month</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1964-01</td>\n",
              "      <td>2815.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1964-02</td>\n",
              "      <td>2672.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1964-03</td>\n",
              "      <td>2755.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1964-04</td>\n",
              "      <td>2721.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1964-05</td>\n",
              "      <td>2946.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Month   Sales\n",
              "0  1964-01  2815.0\n",
              "1  1964-02  2672.0\n",
              "2  1964-03  2755.0\n",
              "3  1964-04  2721.0\n",
              "4  1964-05  2946.0"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "5NADcbBiYCYX",
        "outputId": "7c7a50af-1103-409c-fcce-f2a55994e2e6"
      },
      "source": [
        "# Lets drop the last two records as these records are NaN\n",
        "\n",
        "df.drop(105, axis = 0, inplace = True)\n",
        "df.drop(106, axis = 0, inplace = True)\n",
        "df.tail()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>Month</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>1972-05</td>\n",
              "      <td>4618.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>1972-06</td>\n",
              "      <td>5312.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>1972-07</td>\n",
              "      <td>4298.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>1972-08</td>\n",
              "      <td>1413.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>1972-09</td>\n",
              "      <td>5877.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Month   Sales\n",
              "100  1972-05  4618.0\n",
              "101  1972-06  5312.0\n",
              "102  1972-07  4298.0\n",
              "103  1972-08  1413.0\n",
              "104  1972-09  5877.0"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XBBWAUFfYekq",
        "outputId": "0f58f808-6d82-4aaa-c54f-a92c29f2206e"
      },
      "source": [
        "# Lets see the datatypes of the features\n",
        "\n",
        "df.dtypes"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month     object\n",
              "Sales    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "Wv74wD70YmfC",
        "outputId": "bd848c3b-61b3-4911-af12-ae0948586ac2"
      },
      "source": [
        "# Lets convert \"Month\" into datetime format\n",
        "\n",
        "df[\"Month\"] = pd.to_datetime(df[\"Month\"])\n",
        "df.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\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>Month</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1964-01-01</td>\n",
              "      <td>2815.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1964-02-01</td>\n",
              "      <td>2672.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1964-03-01</td>\n",
              "      <td>2755.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1964-04-01</td>\n",
              "      <td>2721.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1964-05-01</td>\n",
              "      <td>2946.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Month   Sales\n",
              "0 1964-01-01  2815.0\n",
              "1 1964-02-01  2672.0\n",
              "2 1964-03-01  2755.0\n",
              "3 1964-04-01  2721.0\n",
              "4 1964-05-01  2946.0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_OVswyCRY9DS",
        "outputId": "c41b28c7-515a-4eee-85a2-99c51b93614c"
      },
      "source": [
        "# Lets check the datatypes now\n",
        "\n",
        "df.dtypes"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month    datetime64[ns]\n",
              "Sales           float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "0zGZyMd-ZBc-",
        "outputId": "09b25d1b-ec5d-48c3-8494-60e8e0c0cf25"
      },
      "source": [
        "# Lets set \"Month\" as the index\n",
        "\n",
        "df.set_index(\"Month\", inplace = True)\n",
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1964-01-01</th>\n",
              "      <td>2815.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-02-01</th>\n",
              "      <td>2672.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-03-01</th>\n",
              "      <td>2755.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-04-01</th>\n",
              "      <td>2721.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-05-01</th>\n",
              "      <td>2946.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Sales\n",
              "Month             \n",
              "1964-01-01  2815.0\n",
              "1964-02-01  2672.0\n",
              "1964-03-01  2755.0\n",
              "1964-04-01  2721.0\n",
              "1964-05-01  2946.0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "mP8WC2eEZNTn",
        "outputId": "4476fe50-4bf7-45fd-cf6b-c8e2a7794f54"
      },
      "source": [
        "# Lets see the description of the dataset\n",
        "\n",
        "df.describe()"
      ],
      "execution_count": 11,
      "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>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>105.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4761.152381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2553.502601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1413.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3113.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4217.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5221.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>13916.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Sales\n",
              "count    105.000000\n",
              "mean    4761.152381\n",
              "std     2553.502601\n",
              "min     1413.000000\n",
              "25%     3113.000000\n",
              "50%     4217.000000\n",
              "75%     5221.000000\n",
              "max    13916.000000"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Urt3M17_ZV2i"
      },
      "source": [
        "### Visualize the Time Series Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "sofDoN4vZUWj",
        "outputId": "e59706fe-6d41-4a9d-9b1a-b839735e153b"
      },
      "source": [
        "df.plot();"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vbmq46YSZkDA"
      },
      "source": [
        "### Testing the data for Stationarity\n",
        "In this step we will perform the Augmented Dicky Fuller's test."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfG0rTE7ZfEY",
        "outputId": "ad1eef11-3823-4a08-f94e-76a9069b35bf"
      },
      "source": [
        "from statsmodels.tsa.stattools import adfuller"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EA99Pr7IZ8hH"
      },
      "source": [
        "test_results = adfuller(df[\"Sales\"])"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QhJmT0oeaCdT"
      },
      "source": [
        "#Ho: It is non stationary\n",
        "#H1: It is stationary\n",
        "\n",
        "def adfuller_test(sales):\n",
        "    result=adfuller(sales)\n",
        "    labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
        "    for value,label in zip(result,labels):\n",
        "        print(label+' : '+str(value) )\n",
        "    if result[1] <= 0.05:\n",
        "        print(\"strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\")\n",
        "    else:\n",
        "        print(\"weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \")"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LpGcmum3bxv8",
        "outputId": "755b3299-607f-4652-9c9e-94f979e0be25"
      },
      "source": [
        "# Lets see what ADF test gives with our sales column\n",
        "adfuller_test(df[\"Sales\"])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Test Statistic : -1.8335930563276228\n",
            "p-value : 0.363915771660245\n",
            "#Lags Used : 11\n",
            "Number of Observations Used : 93\n",
            "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k_jR4661cTSH"
      },
      "source": [
        "### Differencing\n",
        "Used when the data is non-stationary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l18wYRTacAHO"
      },
      "source": [
        "# Here we will shift the sales value by 1\n",
        "\n",
        "df[\"Sales First Difference\"] = df[\"Sales\"] - df[\"Sales\"].shift(1)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYvs8ARHKVMK",
        "outputId": "807758db-0404-4a35-c098-df46731b4754"
      },
      "source": [
        "df[\"Sales First Difference\"]"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month\n",
              "1964-01-01       NaN\n",
              "1964-02-01    -143.0\n",
              "1964-03-01      83.0\n",
              "1964-04-01     -34.0\n",
              "1964-05-01     225.0\n",
              "               ...  \n",
              "1972-05-01    -170.0\n",
              "1972-06-01     694.0\n",
              "1972-07-01   -1014.0\n",
              "1972-08-01   -2885.0\n",
              "1972-09-01    4464.0\n",
              "Name: Sales First Difference, Length: 105, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1kcow3EFKgVb"
      },
      "source": [
        "Since, our data is monthly seasonal, we should shift by 12. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_gFOd8tKay5"
      },
      "source": [
        "df[\"Seasonal First Difference\"] = df[\"Sales\"] - df[\"Sales\"].shift(12)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "yTH85MDsLL9c",
        "outputId": "ed2adb27-a06f-4427-b162-706b35d2099f"
      },
      "source": [
        "# Lets check our data\n",
        "\n",
        "df.head(14)"
      ],
      "execution_count": 22,
      "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>Sales</th>\n",
              "      <th>Sales First Difference</th>\n",
              "      <th>Seasonal First Difference</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1964-01-01</th>\n",
              "      <td>2815.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-02-01</th>\n",
              "      <td>2672.0</td>\n",
              "      <td>-143.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-03-01</th>\n",
              "      <td>2755.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-04-01</th>\n",
              "      <td>2721.0</td>\n",
              "      <td>-34.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-05-01</th>\n",
              "      <td>2946.0</td>\n",
              "      <td>225.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-06-01</th>\n",
              "      <td>3036.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-07-01</th>\n",
              "      <td>2282.0</td>\n",
              "      <td>-754.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-08-01</th>\n",
              "      <td>2212.0</td>\n",
              "      <td>-70.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-09-01</th>\n",
              "      <td>2922.0</td>\n",
              "      <td>710.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-10-01</th>\n",
              "      <td>4301.0</td>\n",
              "      <td>1379.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-11-01</th>\n",
              "      <td>5764.0</td>\n",
              "      <td>1463.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-12-01</th>\n",
              "      <td>7312.0</td>\n",
              "      <td>1548.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1965-01-01</th>\n",
              "      <td>2541.0</td>\n",
              "      <td>-4771.0</td>\n",
              "      <td>-274.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1965-02-01</th>\n",
              "      <td>2475.0</td>\n",
              "      <td>-66.0</td>\n",
              "      <td>-197.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Sales  Sales First Difference  Seasonal First Difference\n",
              "Month                                                                \n",
              "1964-01-01  2815.0                     NaN                        NaN\n",
              "1964-02-01  2672.0                  -143.0                        NaN\n",
              "1964-03-01  2755.0                    83.0                        NaN\n",
              "1964-04-01  2721.0                   -34.0                        NaN\n",
              "1964-05-01  2946.0                   225.0                        NaN\n",
              "1964-06-01  3036.0                    90.0                        NaN\n",
              "1964-07-01  2282.0                  -754.0                        NaN\n",
              "1964-08-01  2212.0                   -70.0                        NaN\n",
              "1964-09-01  2922.0                   710.0                        NaN\n",
              "1964-10-01  4301.0                  1379.0                        NaN\n",
              "1964-11-01  5764.0                  1463.0                        NaN\n",
              "1964-12-01  7312.0                  1548.0                        NaN\n",
              "1965-01-01  2541.0                 -4771.0                     -274.0\n",
              "1965-02-01  2475.0                   -66.0                     -197.0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uZB1-NdWLR5K",
        "outputId": "dc10456b-57aa-4d39-8316-c7e259da8edd"
      },
      "source": [
        "# Again test dickey fuller test\n",
        "\n",
        "adfuller_test(df['Seasonal First Difference'].dropna())"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Test Statistic : -7.626619157213164\n",
            "p-value : 2.060579696813685e-11\n",
            "#Lags Used : 0\n",
            "Number of Observations Used : 92\n",
            "strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "uN-AJE-GLnKk",
        "outputId": "810328f5-3731-4abe-947f-1ff1308e1c4b"
      },
      "source": [
        "# Lets plot our stationary data\n",
        "\n",
        "df['Seasonal First Difference'].plot();"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l2COV-s6Lx-T"
      },
      "source": [
        "### Auto Regressive Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "-VowRO8XLt8J",
        "outputId": "0e431f60-cd45-4b0c-ad96-ef98de778fd3"
      },
      "source": [
        "from pandas.plotting import autocorrelation_plot\n",
        "autocorrelation_plot(df['Sales'])\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CMJclr5DN8Aj"
      },
      "source": [
        "#### Final Thoughts on Autocorrelation and Partial Autocorrelation\n",
        "* Identification of an AR model is often best done with the PACF.\n",
        "For an AR model, the theoretical PACF “shuts off” past the order of the model. The phrase “shuts off” means that in theory the partial autocorrelations are equal to 0 beyond that point. Put another way, the number of non-zero partial autocorrelations gives the order of the AR model. By the “order of the model” we mean the most extreme lag of x that is used as a predictor.\n",
        "Identification of an MA model is often best done with the ACF rather than the PACF.\n",
        "\n",
        "* For an MA model, the theoretical PACF does not shut off, but instead tapers toward 0 in some manner. A clearer pattern for an MA model is in the ACF. The ACF will have non-zero autocorrelations only at lags involved in the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "jSmden0UPE_f",
        "outputId": "260ac4af-1b42-4c82-a825-f50707988180"
      },
      "source": [
        "# Lets plot the ACF and the PACF\n",
        "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf \n",
        "\n",
        "fig = plt.figure(figsize=(12,8))\n",
        "ax1 = fig.add_subplot(211)\n",
        "fig = plot_acf(df['Seasonal First Difference'].iloc[13:],lags=40,ax=ax1)  #  The first 13 values are NaN, so we will not include these values\n",
        "ax2 = fig.add_subplot(212)\n",
        "fig = plot_pacf(df['Seasonal First Difference'].iloc[13:],lags=40,ax=ax2)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bh3_4esdQuDs"
      },
      "source": [
        "Here, We will take p = 1, q = 0, d = 1. Where p = AR value, q = MA value and d is the number of times differencing performed to make the data stationary."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "syEvAuFEQRo8"
      },
      "source": [
        "ARIMA model is used for non seasonal data. Here, the data is seasonal so we will apply SARIMAX. But before that lets see how ARIMA works even with this seasonal data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bn_hUwfmPILJ"
      },
      "source": [
        "from statsmodels.tsa.arima_model import ARIMA"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rG2sdYasQo4_",
        "outputId": "fb8942de-fcf2-48ba-94a2-c4a60f8723fc"
      },
      "source": [
        "# Fitting ARIMA model\n",
        "\n",
        "model = ARIMA(df[\"Sales\"], order = (1, 1, 1))\n",
        "model_fit = model.fit()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  % freq, ValueWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  % freq, ValueWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "S0CfVKHWRCOF",
        "outputId": "77680023-efed-45c0-87d1-985fa7e2cafa"
      },
      "source": [
        "# Lets see the summary\n",
        "\n",
        "model_fit.summary()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>ARIMA Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>      <td>D.Sales</td>     <th>  No. Observations:  </th>    <td>104</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>          <td>ARIMA(1, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-951.126</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th> <td>2227.262</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>          <td>Fri, 10 Sep 2021</td> <th>  AIC                </th> <td>1910.251</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>              <td>15:12:33</td>     <th>  BIC                </th> <td>1920.829</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>           <td>02-01-1964</td>    <th>  HQIC               </th> <td>1914.536</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                 <td>- 09-01-1972</td>   <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>         <td>   22.7848</td> <td>   12.405</td> <td>    1.837</td> <td> 0.069</td> <td>   -1.529</td> <td>   47.099</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1.D.Sales</th> <td>    0.4343</td> <td>    0.089</td> <td>    4.866</td> <td> 0.000</td> <td>    0.259</td> <td>    0.609</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1.D.Sales</th> <td>   -1.0000</td> <td>    0.026</td> <td>  -38.503</td> <td> 0.000</td> <td>   -1.051</td> <td>   -0.949</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Roots</caption>\n",
              "<tr>\n",
              "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.1</th> <td>           2.3023</td> <td>          +0.0000j</td> <td>           2.3023</td> <td>           0.0000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.1</th> <td>           1.0000</td> <td>          +0.0000j</td> <td>           1.0000</td> <td>           0.0000</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                             ARIMA Model Results                              \n",
              "==============================================================================\n",
              "Dep. Variable:                D.Sales   No. Observations:                  104\n",
              "Model:                 ARIMA(1, 1, 1)   Log Likelihood                -951.126\n",
              "Method:                       css-mle   S.D. of innovations           2227.262\n",
              "Date:                Fri, 10 Sep 2021   AIC                           1910.251\n",
              "Time:                        15:12:33   BIC                           1920.829\n",
              "Sample:                    02-01-1964   HQIC                          1914.536\n",
              "                         - 09-01-1972                                         \n",
              "=================================================================================\n",
              "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
              "---------------------------------------------------------------------------------\n",
              "const            22.7848     12.405      1.837      0.069      -1.529      47.099\n",
              "ar.L1.D.Sales     0.4343      0.089      4.866      0.000       0.259       0.609\n",
              "ma.L1.D.Sales    -1.0000      0.026    -38.503      0.000      -1.051      -0.949\n",
              "                                    Roots                                    \n",
              "=============================================================================\n",
              "                  Real          Imaginary           Modulus         Frequency\n",
              "-----------------------------------------------------------------------------\n",
              "AR.1            2.3023           +0.0000j            2.3023            0.0000\n",
              "MA.1            1.0000           +0.0000j            1.0000            0.0000\n",
              "-----------------------------------------------------------------------------\n",
              "\"\"\""
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "fBwjD9XpRHOz",
        "outputId": "1336d2de-683b-45d9-8236-8066330d7025"
      },
      "source": [
        "# Model Forecast\n",
        "df[\"Forecast\"] = model_fit.predict(start = 90, end = 103, dynamic = True)\n",
        "\n",
        "# Lets plot\n",
        "df[[\"Sales\", \"Forecast\"]].plot(figsize = (12,8));"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mnXEcGJISR_L"
      },
      "source": [
        "### SARIMAX "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pO-4VK8zSFU4"
      },
      "source": [
        "import statsmodels.api as sm"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "seQuwZNySiSg",
        "outputId": "faaf4694-fe24-4387-b2ca-5ff7477dbbd6"
      },
      "source": [
        "model = sm.tsa.statespace.SARIMAX(df[\"Sales\"], order = (1,1,1), seasonal_order = (1,1,1,12))  # 12 is the number of shifts we did earlier\n",
        "results = model.fit()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  % freq, ValueWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "yJVxiRsuS9eP",
        "outputId": "65bc9071-f128-43ca-9407-4afc6e32c0d2"
      },
      "source": [
        "# Forecast\n",
        "df[\"Forecast\"] = results.predict(start = 90, end = 103, dynamic = True)\n",
        "\n",
        "# Lets Visualize\n",
        "df[[\"Sales\", \"Forecast\"]].plot(figsize = (12,8));"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yzPDxGcrUJpS"
      },
      "source": [
        "### Future Forecast using SARIMAX"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rw9MT9kTTs8"
      },
      "source": [
        "# Lets create the data points \n",
        "\n",
        "from pandas.tseries.offsets import DateOffset\n",
        "future_dates = [df.index[-1] + DateOffset(months = x) for x in range(0,24)]"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "br60L5ueUq3L"
      },
      "source": [
        "# Lets convert into a Data frame\n",
        "\n",
        "future_dataset_df = pd.DataFrame(index = future_dates[1:], columns = df.columns)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "u0BAkO0UU_az",
        "outputId": "21be5cb0-52d6-4e79-ca3b-6929c5021b69"
      },
      "source": [
        "# Lets check our future dataframe\n",
        "\n",
        "future_dataset_df.tail()"
      ],
      "execution_count": 42,
      "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>Sales</th>\n",
              "      <th>Sales First Difference</th>\n",
              "      <th>Seasonal First Difference</th>\n",
              "      <th>Forecast</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1974-04-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-05-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-06-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-07-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-08-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Sales Sales First Difference Seasonal First Difference Forecast\n",
              "1974-04-01   NaN                    NaN                       NaN      NaN\n",
              "1974-05-01   NaN                    NaN                       NaN      NaN\n",
              "1974-06-01   NaN                    NaN                       NaN      NaN\n",
              "1974-07-01   NaN                    NaN                       NaN      NaN\n",
              "1974-08-01   NaN                    NaN                       NaN      NaN"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOJXcvnJVFk5"
      },
      "source": [
        "# Lets concat the future dataset with our actual dataset\n",
        "\n",
        "future_df = pd.concat([df, future_dataset_df])"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "UAa222GHVUen",
        "outputId": "26724acd-bebd-4009-90ee-59b4f8de0204"
      },
      "source": [
        "# Forecasting\n",
        "future_df['Forecast'] = results.predict(start = 104, end = 120, dynamic = True)\n",
        "\n",
        "# Lets Visualize\n",
        "future_df[[\"Sales\", \"Forecast\"]].plot(figsize = (12,8));"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}