pree62/forecastingcoco_assgn.ipynb

## forecastingcoco_assgn.ipynb
{
  "cells": [
    {
      "metadata": {
        "id": "U7Sd8uIuxyXS"
      },
      "cell_type": "markdown",
      "source": "# Forecast the CocaCola prices and Airlines Passengers data set. Prepare a document for each model explaining \nhow many dummy variables you have created and RMSE value for each model. Finally which model you will use for \nForecasting.\n"
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M_RxeurTf9E-",
        "outputId": "620c9cc7-0317-4a67-bf43-581bd4a0cd0a",
        "trusted": false
      },
      "cell_type": "code",
      "source": "!pip install pmdarima",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\nCollecting pmdarima\n  Downloading pmdarima-2.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl (1.8 MB)\n\u001b[K     |████████████████████████████████| 1.8 MB 5.2 MB/s \n\u001b[?25hRequirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.3.5)\nRequirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.7.3)\nRequirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.0.2)\nCollecting statsmodels>=0.13.2\n  Downloading statsmodels-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)\n\u001b[K     |████████████████████████████████| 9.8 MB 38.6 MB/s \n\u001b[?25hRequirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.24.3)\nRequirement already satisfied: Cython!=0.29.18,!=0.29.31,>=0.29 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.29.32)\nRequirement already satisfied: numpy>=1.21 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.21.6)\nRequirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (57.4.0)\nRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.2.0)\nRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima) (2.8.2)\nRequirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima) (2022.5)\nRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19->pmdarima) (1.15.0)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->pmdarima) (3.1.0)\nRequirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.13.2->pmdarima) (21.3)\nRequirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.13.2->pmdarima) (0.5.3)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=21.3->statsmodels>=0.13.2->pmdarima) (3.0.9)\nInstalling collected packages: statsmodels, pmdarima\n  Attempting uninstall: statsmodels\n    Found existing installation: statsmodels 0.12.2\n    Uninstalling statsmodels-0.12.2:\n      Successfully uninstalled statsmodels-0.12.2\nSuccessfully installed pmdarima-2.0.1 statsmodels-0.13.2\n"
        }
      ]
    },
    {
      "metadata": {
        "id": "5mSKLImDgGa8",
        "trusted": false
      },
      "cell_type": "code",
      "source": "from pmdarima import auto_arima",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nXLvA6jFgImv",
        "trusted": false
      },
      "cell_type": "code",
      "source": "import warnings\nwarnings.filterwarnings(\"ignore\")",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nofGGKONgK3u",
        "trusted": false
      },
      "cell_type": "code",
      "source": "import pandas as pd\nfrom datetime import date, timedelta\nimport datetime\nimport matplotlib.pyplot as plt\nplt.style.use('fivethirtyeight')\nfrom statsmodels.tsa.seasonal import seasonal_decompose\nfrom statsmodels.graphics.tsaplots import plot_pacf\nfrom statsmodels.tsa.arima_model import ARIMA\nimport statsmodels.api as sm\nimport numpy as np",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TBSZiH_ggQ20",
        "trusted": false
      },
      "cell_type": "code",
      "source": "coco = pd.read_excel('CocaCola_Sales_Rawdata.xlsx')",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "B1fu6n20gSOo",
        "outputId": "7cf73dcb-8e29-43ee-f88f-508d8e53bf2f",
        "trusted": false
      },
      "cell_type": "code",
      "source": "coco",
      "execution_count": null,
      "outputs": [
        {
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            "text/plain": "   Quarter        Sales\n0    Q1_86  1734.827000\n1    Q2_86  2244.960999\n2    Q3_86  2533.804993\n3    Q4_86  2154.962997\n4    Q1_87  1547.818996\n5    Q2_87  2104.411995\n6    Q3_87  2014.362999\n7    Q4_87  1991.746998\n8    Q1_88  1869.049999\n9    Q2_88  2313.631996\n10   Q3_88  2128.320000\n11   Q4_88  2026.828999\n12   Q1_89  1910.603996\n13   Q2_89  2331.164993\n14   Q3_89  2206.549995\n15   Q4_89  2173.967995\n16   Q1_90  2148.278000\n17   Q2_90  2739.307999\n18   Q3_90  2792.753998\n19   Q4_90  2556.009995\n20   Q1_91  2480.973999\n21   Q2_91  3039.522995\n22   Q3_91  3172.115997\n23   Q4_91  2879.000999\n24   Q1_92  2772.000000\n25   Q2_92  3550.000000\n26   Q3_92  3508.000000\n27   Q4_92  3243.859993\n28   Q1_93  3056.000000\n29   Q2_93  3899.000000\n30   Q3_93  3629.000000\n31   Q4_93  3373.000000\n32   Q1_94  3352.000000\n33   Q2_94  4342.000000\n34   Q3_94  4461.000000\n35   Q4_94  4017.000000\n36   Q1_95  3854.000000\n37   Q2_95  4936.000000\n38   Q3_95  4895.000000\n39   Q4_95  4333.000000\n40   Q1_96  4194.000000\n41   Q2_96  5253.000000"
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "id": "wD-VYOimgbiW",
        "trusted": false
      },
      "cell_type": "code",
      "source": "import plotly.express as px",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fmG_k5ZiiF0Z",
        "trusted": false
      },
      "cell_type": "code",
      "source": "figure = px.line(coco, x=\"Quarter\", \n                 y=\"Sales\", \n                 title='Quarterly Sales')",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ZyMlO_uRgtcq",
        "outputId": "e149504f-57d9-4f23-99e6-45565c114653",
        "trusted": false
      },
      "cell_type": "code",
      "source": "figure.show()",
      "execution_count": null,
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fAG11KlsLBP_",
        "outputId": "825791e7-9fad-484d-8b10-5c0a1fb163fa",
        "trusted": false
      },
      "cell_type": "code",
      "source": "coco.info()",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 42 entries, 0 to 41\nData columns (total 2 columns):\n #   Column   Non-Null Count  Dtype  \n---  ------   --------------  -----  \n 0   Quarter  42 non-null     object \n 1   Sales    42 non-null     float64\ndtypes: float64(1), object(1)\nmemory usage: 800.0+ bytes\n"
        }
      ]
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "UBi9zI8KLRoN",
        "outputId": "639543c4-f723-4461-a6c6-708a6c57021e",
        "trusted": false
      },
      "cell_type": "code",
      "source": "## preprocessing\nquarter=['Q1','Q2','Q3','Q4']\nn=coco['Quarter'][0]\nn[0:2] #Triming the year ",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": "'Q1'"
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "id": "dP3ve6OhLyaH",
        "trusted": false
      },
      "cell_type": "code",
      "source": "coco['quarter']=0\nfor i in range(42):\n    n=coco['Quarter'][i]\n    coco['quarter'][i]=n[0:2]\n    dummy=pd.DataFrame(pd.get_dummies(coco['quarter']))\n    data1=pd.concat((coco,dummy),axis=1)\nt= np.arange(1,43)\ndata1['t']=t\ndata1['t_square']=data1['t']*data1['t']\nlog_Sales=np.log(data1['Sales'])\ndata1['log_Sales']=log_Sales",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "N-g0YL7oMdmi",
        "outputId": "844bf866-7813-412a-8bab-092ba3cdef12",
        "trusted": false
      },
      "cell_type": "code",
      "source": "data1 ",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": "\n  <div id=\"df-b1902417-69a4-4f39-a6ee-df90388b190a\">\n    <div class=\"colab-df-container\">\n      <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>Quarter</th>\n      <th>Sales</th>\n      <th>quarter</th>\n      <th>Q1</th>\n      <th>Q2</th>\n      <th>Q3</th>\n      <th>Q4</th>\n      <th>t</th>\n      <th>t_square</th>\n      <th>log_Sales</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Q1_86</td>\n      <td>1734.827000</td>\n      <td>Q1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>7.458663</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Q2_86</td>\n      <td>2244.960999</td>\n      <td>Q2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>4</td>\n      <td>7.716443</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Q3_86</td>\n      <td>2533.804993</td>\n      <td>Q3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>9</td>\n      <td>7.837477</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Q4_86</td>\n      <td>2154.962997</td>\n      <td>Q4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>16</td>\n      <td>7.675529</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Q1_87</td>\n      <td>1547.818996</td>\n      <td>Q1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>25</td>\n      <td>7.344602</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Q2_87</td>\n      <td>2104.411995</td>\n      <td>Q2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>36</td>\n      <td>7.651791</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Q3_87</td>\n      <td>2014.362999</td>\n      <td>Q3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7</td>\n      <td>49</td>\n      <td>7.608058</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Q4_87</td>\n      <td>1991.746998</td>\n      <td>Q4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>8</td>\n      <td>64</td>\n      <td>7.596767</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Q1_88</td>\n      <td>1869.049999</td>\n      <td>Q1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>81</td>\n      <td>7.533186</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Q2_88</td>\n      <td>2313.631996</td>\n      <td>Q2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>10</td>\n      <td>100</td>\n      <td>7.746574</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Q3_88</td>\n      <td>2128.320000</td>\n      <td>Q3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>11</td>\n      <td>121</td>\n      <td>7.663088</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Q4_88</td>\n      <td>2026.828999</td>\n      <td>Q4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>12</td>\n      <td>144</td>\n      <td>7.614228</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Q1_89</td>\n      <td>1910.603996</td>\n      <td>Q1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>13</td>\n      <td>169</td>\n      <td>7.555175</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Q2_89</td>\n      <td>2331.164993</td>\n      <td>Q2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>14</td>\n      <td>196</td>\n      <td>7.754123</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Q3_89</td>\n      <td>2206.549995</td>\n      <td>Q3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>15</td>\n      <td>225</td>\n      <td>7.699185</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Q4_89</td>\n      <td>2173.967995</td>\n      <td>Q4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>16</td>\n      <td>256</td>\n      <td>7.684309</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Q1_90</td>\n      <td>2148.278000</td>\n      <td>Q1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>17</td>\n      <td>289</td>\n      <td>7.672422</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Q2_90</td>\n      <td>2739.307999</td>\n      <td>Q2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>18</td>\n      <td>324</td>\n      <td>7.915461</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Q3_90</td>\n      <td>2792.753998</td>\n      <td>Q3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>19</td>\n      <td>361</td>\n      <td>7.934783</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Q4_90</td>\n      <td>2556.009995</td>\n      <td>Q4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>20</td>\n      <td>400</td>\n      <td>7.846203</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Q1_91</td>\n      <td>2480.973999</td>\n      <td>Q1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>21</td>\n      <td>441</td>\n      <td>7.816407</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Q2_91</td>\n      <td>3039.522995</td>\n      <td>Q2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>22</td>\n      <td>484</td>\n      <td>8.019456</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Q3_91</td>\n      <td>3172.115997</td>\n      <td>Q3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>23</td>\n      <td>529</td>\n      <td>8.062154</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Q4_91</td>\n      <td>2879.000999</td>\n      <td>Q4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>24</td>\n      <td>576</td>\n      <td>7.965199</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Q1_92</td>\n      <td>2772.000000</td>\n      <td>Q1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>25</td>\n      <td>625</td>\n      <td>7.927324</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Q2_92</td>\n      <td>3550.000000</td>\n      <td>Q2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>26</td>\n      <td>676</td>\n      <td>8.174703</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Q3_92</td>\n      <td>3508.000000</td>\n      <td>Q3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>27</td>\n      <td>729</td>\n      <td>8.162801</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Q4_92</td>\n      <td>3243.859993</td>\n      <td>Q4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>28</td>\n      <td>784</td>\n      <td>8.084519</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Q1_93</td>\n      <td>3056.000000</td>\n      <td>Q1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>29</td>\n      <td>841</td>\n      <td>8.024862</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Q2_93</td>\n      <td>3899.000000</td>\n      <td>Q2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>30</td>\n      <td>900</td>\n      <td>8.268475</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Q3_93</td>\n      <td>3629.000000</td>\n      <td>Q3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>31</td>\n      <td>961</td>\n      <td>8.196712</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Q4_93</td>\n      <td>3373.000000</td>\n      <td>Q4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>32</td>\n      <td>1024</td>\n      <td>8.123558</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Q1_94</td>\n      <td>3352.000000</td>\n      <td>Q1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>33</td>\n      <td>1089</td>\n      <td>8.117312</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Q2_94</td>\n      <td>4342.000000</td>\n      <td>Q2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>34</td>\n      <td>1156</td>\n      <td>8.376090</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>Q3_94</td>\n      <td>4461.000000</td>\n      <td>Q3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>35</td>\n      <td>1225</td>\n      <td>8.403128</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>Q4_94</td>\n      <td>4017.000000</td>\n      <td>Q4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>36</td>\n      <td>1296</td>\n      <td>8.298291</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>Q1_95</td>\n      <td>3854.000000</td>\n      <td>Q1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>37</td>\n      <td>1369</td>\n      <td>8.256867</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>Q2_95</td>\n      <td>4936.000000</td>\n      <td>Q2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>38</td>\n      <td>1444</td>\n      <td>8.504311</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Q3_95</td>\n      <td>4895.000000</td>\n      <td>Q3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>39</td>\n      <td>1521</td>\n      <td>8.495970</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Q4_95</td>\n      <td>4333.000000</td>\n      <td>Q4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>40</td>\n      <td>1600</td>\n      <td>8.374015</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>Q1_96</td>\n      <td>4194.000000</td>\n      <td>Q1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>41</td>\n      <td>1681</td>\n      <td>8.341410</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>Q2_96</td>\n      <td>5253.000000</td>\n      <td>Q2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>42</td>\n      <td>1764</td>\n      <td>8.566555</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1902417-69a4-4f39-a6ee-df90388b190a')\"\n              title=\"Convert this dataframe to an interactive table.\"\n              style=\"display:none;\">\n        \n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n       width=\"24px\">\n    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n  </svg>\n      </button>\n      \n  <style>\n    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            "text/plain": "   Quarter        Sales quarter  Q1  Q2  Q3  Q4   t  t_square  log_Sales\n0    Q1_86  1734.827000      Q1   1   0   0   0   1         1   7.458663\n1    Q2_86  2244.960999      Q2   0   1   0   0   2         4   7.716443\n2    Q3_86  2533.804993      Q3   0   0   1   0   3         9   7.837477\n3    Q4_86  2154.962997      Q4   0   0   0   1   4        16   7.675529\n4    Q1_87  1547.818996      Q1   1   0   0   0   5        25   7.344602\n5    Q2_87  2104.411995      Q2   0   1   0   0   6        36   7.651791\n6    Q3_87  2014.362999      Q3   0   0   1   0   7        49   7.608058\n7    Q4_87  1991.746998      Q4   0   0   0   1   8        64   7.596767\n8    Q1_88  1869.049999      Q1   1   0   0   0   9        81   7.533186\n9    Q2_88  2313.631996      Q2   0   1   0   0  10       100   7.746574\n10   Q3_88  2128.320000      Q3   0   0   1   0  11       121   7.663088\n11   Q4_88  2026.828999      Q4   0   0   0   1  12       144   7.614228\n12   Q1_89  1910.603996      Q1   1   0   0   0  13       169   7.555175\n13   Q2_89  2331.164993      Q2   0   1   0   0  14       196   7.754123\n14   Q3_89  2206.549995      Q3   0   0   1   0  15       225   7.699185\n15   Q4_89  2173.967995      Q4   0   0   0   1  16       256   7.684309\n16   Q1_90  2148.278000      Q1   1   0   0   0  17       289   7.672422\n17   Q2_90  2739.307999      Q2   0   1   0   0  18       324   7.915461\n18   Q3_90  2792.753998      Q3   0   0   1   0  19       361   7.934783\n19   Q4_90  2556.009995      Q4   0   0   0   1  20       400   7.846203\n20   Q1_91  2480.973999      Q1   1   0   0   0  21       441   7.816407\n21   Q2_91  3039.522995      Q2   0   1   0   0  22       484   8.019456\n22   Q3_91  3172.115997      Q3   0   0   1   0  23       529   8.062154\n23   Q4_91  2879.000999      Q4   0   0   0   1  24       576   7.965199\n24   Q1_92  2772.000000      Q1   1   0   0   0  25       625   7.927324\n25   Q2_92  3550.000000      Q2   0   1   0   0  26       676   8.174703\n26   Q3_92  3508.000000      Q3   0   0   1   0  27       729   8.162801\n27   Q4_92  3243.859993      Q4   0   0   0   1  28       784   8.084519\n28   Q1_93  3056.000000      Q1   1   0   0   0  29       841   8.024862\n29   Q2_93  3899.000000      Q2   0   1   0   0  30       900   8.268475\n30   Q3_93  3629.000000      Q3   0   0   1   0  31       961   8.196712\n31   Q4_93  3373.000000      Q4   0   0   0   1  32      1024   8.123558\n32   Q1_94  3352.000000      Q1   1   0   0   0  33      1089   8.117312\n33   Q2_94  4342.000000      Q2   0   1   0   0  34      1156   8.376090\n34   Q3_94  4461.000000      Q3   0   0   1   0  35      1225   8.403128\n35   Q4_94  4017.000000      Q4   0   0   0   1  36      1296   8.298291\n36   Q1_95  3854.000000      Q1   1   0   0   0  37      1369   8.256867\n37   Q2_95  4936.000000      Q2   0   1   0   0  38      1444   8.504311\n38   Q3_95  4895.000000      Q3   0   0   1   0  39      1521   8.495970\n39   Q4_95  4333.000000      Q4   0   0   0   1  40      1600   8.374015\n40   Q1_96  4194.000000      Q1   1   0   0   0  41      1681   8.341410\n41   Q2_96  5253.000000      Q2   0   1   0   0  42      1764   8.566555"
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "id": "a5sT_BvKNCvH",
        "trusted": false
      },
      "cell_type": "code",
      "source": "from sklearn.preprocessing import LabelEncoder\nle = LabelEncoder()\ndata1['quarter']= le.fit_transform(data1['quarter'])\ndata1['quarter']=data1['quarter']+1 ",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xGpjnNXpRLkX",
        "trusted": false
      },
      "cell_type": "code",
      "source": "coco = data1",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jTOiMeAPM2Vy",
        "trusted": false
      },
      "cell_type": "code",
      "source": "result = seasonal_decompose(coco['Sales'], model ='multiplicative',period= 4)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "t5F92mAhOsLq",
        "outputId": "0ca994b5-3a54-4f18-a6cb-75fb3fd4b938",
        "trusted": false
      },
      "cell_type": "code",
      "source": "result.plot()",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 4 Axes>"
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 4 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8R1wHk2O70Z",
        "outputId": "2767e679-18dd-4f1f-8f74-a804521c8acd",
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Fit auto_arima function to AirPassengers dataset\nstepwise_fit = auto_arima(coco['Sales'], start_p = 1, start_q = 1,\n                          max_p = 3, max_q = 3, m = 3, # one Quarter = 3 momths\n                          start_P = 0, seasonal = True,\n                          d = None, D = 1, trace = True,\n                          error_action ='ignore',   # we don't want to know if an order does not work\n                          suppress_warnings = True,  # we don't want convergence warnings\n                          stepwise = True)",
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Performing stepwise search to minimize aic\n ARIMA(1,1,1)(0,1,1)[3]             : AIC=inf, Time=0.11 sec\n ARIMA(0,1,0)(0,1,0)[3]             : AIC=600.643, Time=0.01 sec\n ARIMA(1,1,0)(1,1,0)[3]             : AIC=599.807, Time=0.06 sec\n ARIMA(0,1,1)(0,1,1)[3]             : AIC=inf, Time=0.10 sec\n ARIMA(1,1,0)(0,1,0)[3]             : AIC=599.868, Time=0.04 sec\n ARIMA(1,1,0)(2,1,0)[3]             : AIC=573.022, Time=0.21 sec\n ARIMA(1,1,0)(2,1,1)[3]             : AIC=560.959, Time=0.23 sec\n ARIMA(1,1,0)(1,1,1)[3]             : AIC=inf, Time=0.12 sec\n ARIMA(1,1,0)(2,1,2)[3]             : AIC=556.145, Time=0.46 sec\n ARIMA(1,1,0)(1,1,2)[3]             : AIC=inf, Time=0.42 sec\n ARIMA(0,1,0)(2,1,2)[3]             : AIC=inf, Time=0.28 sec\n ARIMA(2,1,0)(2,1,2)[3]             : AIC=inf, Time=0.50 sec\n ARIMA(1,1,1)(2,1,2)[3]             : AIC=557.526, Time=0.41 sec\n ARIMA(0,1,1)(2,1,2)[3]             : AIC=inf, Time=0.23 sec\n ARIMA(2,1,1)(2,1,2)[3]             : AIC=inf, Time=0.46 sec\n ARIMA(1,1,0)(2,1,2)[3] intercept   : AIC=557.443, Time=0.44 sec\n\nBest model:  ARIMA(1,1,0)(2,1,2)[3]          \nTotal fit time: 4.120 seconds\n"
        }
      ]
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "vhHPS8_WPD3i",
        "outputId": "bc42d8fa-bcf7-4374-f069-27955124d9ed",
        "trusted": false
      },
      "cell_type": "code",
      "source": "stepwise_fit.summary()",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>SARIMAX Results</caption>\n<tr>\n  <th>Dep. Variable:</th>                    <td>y</td>                 <th>  No. Observations:  </th>    <td>42</td>   \n</tr>\n<tr>\n  <th>Model:</th>           <td>SARIMAX(1, 1, 0)x(2, 1, [1, 2], 3)</td> <th>  Log Likelihood     </th> <td>-272.072</td>\n</tr>\n<tr>\n  <th>Date:</th>                     <td>Fri, 28 Oct 2022</td>          <th>  AIC                </th>  <td>556.145</td>\n</tr>\n<tr>\n  <th>Time:</th>                         <td>06:26:43</td>              <th>  BIC                </th>  <td>565.970</td>\n</tr>\n<tr>\n  <th>Sample:</th>                           <td>0</td>                 <th>  HQIC               </th>  <td>559.641</td>\n</tr>\n<tr>\n  <th></th>                                <td> - 42</td>               <th>                     </th>     <td> </td>   \n</tr>\n<tr>\n  <th>Covariance Type:</th>                 <td>opg</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>ar.L1</th>   <td>   -0.4561</td> <td>    0.174</td> <td>   -2.620</td> <td> 0.009</td> <td>   -0.797</td> <td>   -0.115</td>\n</tr>\n<tr>\n  <th>ar.S.L3</th> <td>    0.0231</td> <td>    0.186</td> <td>    0.124</td> <td> 0.901</td> <td>   -0.342</td> <td>    0.388</td>\n</tr>\n<tr>\n  <th>ar.S.L6</th> <td>   -0.8840</td> <td>    0.073</td> <td>  -12.134</td> <td> 0.000</td> <td>   -1.027</td> <td>   -0.741</td>\n</tr>\n<tr>\n  <th>ma.S.L3</th> <td>   -1.2514</td> <td>    0.205</td> <td>   -6.095</td> <td> 0.000</td> <td>   -1.654</td> <td>   -0.849</td>\n</tr>\n<tr>\n  <th>ma.S.L6</th> <td>    0.6592</td> <td>    0.281</td> <td>    2.348</td> <td> 0.019</td> <td>    0.109</td> <td>    1.209</td>\n</tr>\n<tr>\n  <th>sigma2</th>  <td> 6.483e+04</td> <td> 2.39e+04</td> <td>    2.714</td> <td> 0.007</td> <td>  1.8e+04</td> <td> 1.12e+05</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Ljung-Box (L1) (Q):</th>     <td>0.16</td> <th>  Jarque-Bera (JB):  </th> <td>0.54</td> \n</tr>\n<tr>\n  <th>Prob(Q):</th>                <td>0.69</td> <th>  Prob(JB):          </th> <td>0.76</td> \n</tr>\n<tr>\n  <th>Heteroskedasticity (H):</th> <td>0.99</td> <th>  Skew:              </th> <td>-0.09</td>\n</tr>\n<tr>\n  <th>Prob(H) (two-sided):</th>    <td>0.99</td> <th>  Kurtosis:          </th> <td>2.45</td> \n</tr>\n</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).",
            "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                                       SARIMAX Results                                        \n==============================================================================================\nDep. Variable:                                      y   No. Observations:                   42\nModel:             SARIMAX(1, 1, 0)x(2, 1, [1, 2], 3)   Log Likelihood                -272.072\nDate:                                Fri, 28 Oct 2022   AIC                            556.145\nTime:                                        06:26:43   BIC                            565.970\nSample:                                             0   HQIC                           559.641\n                                                 - 42                                         \nCovariance Type:                                  opg                                         \n==============================================================================\n                 coef    std err          z      P>|z|      [0.025      0.975]\n------------------------------------------------------------------------------\nar.L1         -0.4561      0.174     -2.620      0.009      -0.797      -0.115\nar.S.L3        0.0231      0.186      0.124      0.901      -0.342       0.388\nar.S.L6       -0.8840      0.073    -12.134      0.000      -1.027      -0.741\nma.S.L3       -1.2514      0.205     -6.095      0.000      -1.654      -0.849\nma.S.L6        0.6592      0.281      2.348      0.019       0.109       1.209\nsigma2      6.483e+04   2.39e+04      2.714      0.007     1.8e+04    1.12e+05\n===================================================================================\nLjung-Box (L1) (Q):                   0.16   Jarque-Bera (JB):                 0.54\nProb(Q):                              0.69   Prob(JB):                         0.76\nHeteroskedasticity (H):               0.99   Skew:                            -0.09\nProb(H) (two-sided):                  0.99   Kurtosis:                         2.45\n===================================================================================\n\nWarnings:\n[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n\"\"\""
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "id": "UaL8bGLaPJGY",
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Split data into train / test sets\ntrain = coco.iloc[:len(coco)-4]\ntest = coco.iloc[len(coco)-4:] # set one year(12 months) for testing\n  ",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TVi3DR16PbFp",
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Fit a SARIMAX(0, 1, 0)x(0, 1, 0, 12) on the training set\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lMhj1hF8PmLW",
        "trusted": false
      },
      "cell_type": "code",
      "source": "model = SARIMAX(train['Sales'],order = (1, 1, 0), seasonal_order =(2, 1, [1, 2], 3))",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "7GwVUt9TPyyD",
        "outputId": "56072b06-b5ff-4831-c186-1ebe28f06dc1",
        "trusted": false
      },
      "cell_type": "code",
      "source": "result = model.fit()\nresult.summary()",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>SARIMAX Results</caption>\n<tr>\n  <th>Dep. Variable:</th>                  <td>Sales</td>               <th>  No. Observations:  </th>    <td>38</td>   \n</tr>\n<tr>\n  <th>Model:</th>           <td>SARIMAX(1, 1, 0)x(2, 1, [1, 2], 3)</td> <th>  Log Likelihood     </th> <td>-245.360</td>\n</tr>\n<tr>\n  <th>Date:</th>                     <td>Fri, 28 Oct 2022</td>          <th>  AIC                </th>  <td>502.719</td>\n</tr>\n<tr>\n  <th>Time:</th>                         <td>06:27:25</td>              <th>  BIC                </th>  <td>511.877</td>\n</tr>\n<tr>\n  <th>Sample:</th>                           <td>0</td>                 <th>  HQIC               </th>  <td>505.842</td>\n</tr>\n<tr>\n  <th></th>                                <td> - 38</td>               <th>                     </th>     <td> </td>   \n</tr>\n<tr>\n  <th>Covariance Type:</th>                 <td>opg</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>ar.L1</th>   <td>   -0.4225</td> <td>    0.211</td> <td>   -2.004</td> <td> 0.045</td> <td>   -0.836</td> <td>   -0.009</td>\n</tr>\n<tr>\n  <th>ar.S.L3</th> <td>    0.0352</td> <td>    0.220</td> <td>    0.160</td> <td> 0.873</td> <td>   -0.396</td> <td>    0.466</td>\n</tr>\n<tr>\n  <th>ar.S.L6</th> <td>   -0.8488</td> <td>    0.095</td> <td>   -8.929</td> <td> 0.000</td> <td>   -1.035</td> <td>   -0.662</td>\n</tr>\n<tr>\n  <th>ma.S.L3</th> <td>   -1.2617</td> <td>    0.232</td> <td>   -5.435</td> <td> 0.000</td> <td>   -1.717</td> <td>   -0.807</td>\n</tr>\n<tr>\n  <th>ma.S.L6</th> <td>    0.6243</td> <td>    0.311</td> <td>    2.006</td> <td> 0.045</td> <td>    0.014</td> <td>    1.234</td>\n</tr>\n<tr>\n  <th>sigma2</th>  <td> 7.273e+04</td> <td> 2.94e+04</td> <td>    2.475</td> <td> 0.013</td> <td> 1.51e+04</td> <td>  1.3e+05</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Ljung-Box (L1) (Q):</th>     <td>0.24</td> <th>  Jarque-Bera (JB):  </th> <td>0.42</td> \n</tr>\n<tr>\n  <th>Prob(Q):</th>                <td>0.62</td> <th>  Prob(JB):          </th> <td>0.81</td> \n</tr>\n<tr>\n  <th>Heteroskedasticity (H):</th> <td>0.95</td> <th>  Skew:              </th> <td>-0.06</td>\n</tr>\n<tr>\n  <th>Prob(H) (two-sided):</th>    <td>0.94</td> <th>  Kurtosis:          </th> <td>2.47</td> \n</tr>\n</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).",
            "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                                       SARIMAX Results                                        \n==============================================================================================\nDep. Variable:                                  Sales   No. Observations:                   38\nModel:             SARIMAX(1, 1, 0)x(2, 1, [1, 2], 3)   Log Likelihood                -245.360\nDate:                                Fri, 28 Oct 2022   AIC                            502.719\nTime:                                        06:27:25   BIC                            511.877\nSample:                                             0   HQIC                           505.842\n                                                 - 38                                         \nCovariance Type:                                  opg                                         \n==============================================================================\n                 coef    std err          z      P>|z|      [0.025      0.975]\n------------------------------------------------------------------------------\nar.L1         -0.4225      0.211     -2.004      0.045      -0.836      -0.009\nar.S.L3        0.0352      0.220      0.160      0.873      -0.396       0.466\nar.S.L6       -0.8488      0.095     -8.929      0.000      -1.035      -0.662\nma.S.L3       -1.2617      0.232     -5.435      0.000      -1.717      -0.807\nma.S.L6        0.6243      0.311      2.006      0.045       0.014       1.234\nsigma2      7.273e+04   2.94e+04      2.475      0.013    1.51e+04     1.3e+05\n===================================================================================\nLjung-Box (L1) (Q):                   0.24   Jarque-Bera (JB):                 0.42\nProb(Q):                              0.62   Prob(JB):                         0.81\nHeteroskedasticity (H):               0.95   Skew:                            -0.06\nProb(H) (two-sided):                  0.94   Kurtosis:                         2.47\n===================================================================================\n\nWarnings:\n[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n\"\"\""
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "id": "aH79CF-nP9Z3",
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Predictions of ARIMA Model against the test set\nstart = len(train)\nend = len(train) + len(test) - 1",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YAn31cH0QAmk",
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Predictions for one-year against the test set\npredictions = result.predict(start, end, typ = 'levels').rename(\"Predictions\")",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "Lqw1ikMbQDTy",
        "outputId": "43637030-2509-4c44-bba3-548e0c78966f",
        "trusted": false
      },
      "cell_type": "code",
      "source": "# plot predictions and actual values\npredictions.plot(legend = True)\ntest['Sales'].plot(legend = True)",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e0bd74f50>"
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "id": "Gzy3_pcpQKF6",
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Load specific evaluation tools\nfrom sklearn.metrics import mean_squared_error\nfrom statsmodels.tools.eval_measures import rmse",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1n1npvJiQN_6",
        "outputId": "3efd52cc-09f8-4399-8d52-18f0ea1f917a",
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Calculate root mean squared error\nrmse(test[\"Sales\"], predictions)\n  ",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": "140.05557171059948"
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "id": "bJri24j4Rzmq",
        "trusted": false
      },
      "cell_type": "code",
      "source": "#Forecast using SARIMA Model\n# Train the model on the full dataset\nmodel = SARIMAX(coco['Sales'],order = (1, 1, 0), seasonal_order =(2, 1, [1, 2], 3))",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oKLbISCeSL_T",
        "trusted": false
      },
      "cell_type": "code",
      "source": "result = model.fit()",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2phs9NWwSPru",
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Forecast for the next 5 years\nforecast = result.predict(start = len(coco), \n                          end = (len(coco)-1) + 5 * 4, \n                          typ = 'levels').rename('Forecast')",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "q_Kf9K97ScNF",
        "outputId": "5b261c94-eeeb-47bb-9b85-4cbc9222aa08",
        "trusted": false
      },
      "cell_type": "code",
      "source": "# Plot the forecast values\ncoco['Sales'].plot(figsize = (12, 5), legend = True)\nforecast.plot(legend = True)",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f1e0bec3750>"
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x360 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3 (ipykernel)",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "!excelR/assignments/Gists/forecastingcoco_assgn.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 1
}