sales_forecasting_using_LGB.ipynb

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    "colab": {
      "name": "sales forecasting using LGB.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
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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/Mishabz4321/MACHINE-LEARNING/blob/main/sales_forecasting_using_LGB.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPFFHIayuck4"
      },
      "source": [
        "#import dependencies\n",
        "\n",
        "import warnings \n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "#Basic packages\n",
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "\n",
        "# Visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "# Time Series\n",
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "from pandas.plotting import autocorrelation_plot\n",
        "from statsmodels.tsa.stattools import adfuller, acf, pacf,arma_order_select_ic\n",
        "import statsmodels.formula.api as smf\n",
        "import statsmodels.tsa.api as smt\n",
        "import statsmodels.api as sm\n",
        "import scipy.stats as scs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVnG2CYSumLc",
        "outputId": "3af276ac-83ac-4597-8158-e8438b766dfa"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "LtDA5f-tuclA",
        "outputId": "fb2b00ef-676b-4ba2-dea2-a324468d0664"
      },
      "source": [
        "train= pd.read_csv('/content/drive/MyDrive/Colab Notebooks/train.csv',parse_dates=['date'])\n",
        "test= pd.read_csv('/content/drive/MyDrive/Colab Notebooks/test.csv',parse_dates=['date'])\n",
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\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>date</th>\n",
              "      <th>store</th>\n",
              "      <th>item</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2013-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013-01-02</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013-01-03</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2013-01-04</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2013-01-05</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  store  item  sales\n",
              "0 2013-01-01      1     1     13\n",
              "1 2013-01-02      1     1     11\n",
              "2 2013-01-03      1     1     14\n",
              "3 2013-01-04      1     1     13\n",
              "4 2013-01-05      1     1     10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Ypu0GHakuclC",
        "outputId": "cef59c64-43cf-42ad-e57d-5067ae56e6bf"
      },
      "source": [
        "train.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\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>date</th>\n",
              "      <th>store</th>\n",
              "      <th>item</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>116001</th>\n",
              "      <td>2015-08-22</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116002</th>\n",
              "      <td>2015-08-23</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116003</th>\n",
              "      <td>2015-08-24</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116004</th>\n",
              "      <td>2015-08-25</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116005</th>\n",
              "      <td>2015-08-26</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             date  store  item  sales\n",
              "116001 2015-08-22      4     7     66\n",
              "116002 2015-08-23      4     7     79\n",
              "116003 2015-08-24      4     7     61\n",
              "116004 2015-08-25      4     7     64\n",
              "116005 2015-08-26      4     7     66"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHCezk8PuclD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6f51b68-9494-4fa7-c231-cb3aac4f644f"
      },
      "source": [
        "data= pd.concat([train, test], sort=False)\n",
        "print(data.head())\n",
        "print('- - - - - - - - - - - - - - - - - - - - -  - - - - -- - ')\n",
        "print(data.tail())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        date  store  item  sales  id\n",
            "0 2013-01-01      1     1   13.0 NaN\n",
            "1 2013-01-02      1     1   11.0 NaN\n",
            "2 2013-01-03      1     1   14.0 NaN\n",
            "3 2013-01-04      1     1   13.0 NaN\n",
            "4 2013-01-05      1     1   10.0 NaN\n",
            "- - - - - - - - - - - - - - - - - - - - -  - - - - -- - \n",
            "            date  store  item  sales       id\n",
            "44995 2018-03-27     10    50    NaN  44995.0\n",
            "44996 2018-03-28     10    50    NaN  44996.0\n",
            "44997 2018-03-29     10    50    NaN  44997.0\n",
            "44998 2018-03-30     10    50    NaN  44998.0\n",
            "44999 2018-03-31     10    50    NaN  44999.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elA-qrN9uclD"
      },
      "source": [
        "## Understanding The Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8FVR4wXjuclD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e7ef4ce-2027-4772-9842-413cc95a5177"
      },
      "source": [
        "train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(116006, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldDY_6fSuclE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "8cba73fc-1091-4180-cbee-71f769a8e810"
      },
      "source": [
        "train.describe().round(2)"
      ],
      "execution_count": null,
      "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>store</th>\n",
              "      <th>item</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>116006.00</td>\n",
              "      <td>116006.00</td>\n",
              "      <td>116006.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.32</td>\n",
              "      <td>3.69</td>\n",
              "      <td>37.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.90</td>\n",
              "      <td>1.84</td>\n",
              "      <td>22.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>20.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>31.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>51.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10.00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>150.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           store       item      sales\n",
              "count  116006.00  116006.00  116006.00\n",
              "mean        5.32       3.69      37.55\n",
              "std         2.90       1.84      22.77\n",
              "min         1.00       1.00       0.00\n",
              "25%         3.00       2.00      20.00\n",
              "50%         5.00       4.00      31.00\n",
              "75%         8.00       5.00      51.00\n",
              "max        10.00       7.00     150.00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usCz8EMruclE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62cf333f-6ab8-47e3-e367-4425ac5de870"
      },
      "source": [
        "train.isnull().sum().any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7gt32s_uclF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ebb96d0-4f8d-44f7-9fb3-934c76493407"
      },
      "source": [
        "train.nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date     1826\n",
              "store      10\n",
              "item        7\n",
              "sales     146\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CqqPpWVEuclF"
      },
      "source": [
        "#### There are 10 different stores and 50 types of items"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19_22h0muclF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75725b10-6800-43fa-aa24-55e278117e6a"
      },
      "source": [
        "train['store'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3     12782\n",
              "2     12782\n",
              "1     12782\n",
              "4     11924\n",
              "10    10956\n",
              "9     10956\n",
              "8     10956\n",
              "7     10956\n",
              "6     10956\n",
              "5     10956\n",
              "Name: store, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2rUI9UPuclG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5923b6e-b838-4bf1-9107-348709270b88"
      },
      "source": [
        "train['item'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6    18260\n",
              "5    18260\n",
              "4    18260\n",
              "3    18260\n",
              "2    18260\n",
              "1    18260\n",
              "7     6446\n",
              "Name: item, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNY0dq_vuclG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08321493-cc57-41e0-e62b-deb7c117edc9"
      },
      "source": [
        "train.groupby('store')['item'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "store\n",
              "1     7\n",
              "2     7\n",
              "3     7\n",
              "4     7\n",
              "5     6\n",
              "6     6\n",
              "7     6\n",
              "8     6\n",
              "9     6\n",
              "10    6\n",
              "Name: item, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eKLH6RgZuclG"
      },
      "source": [
        "#### No.of type of items being sold in each store is 50"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXlLCAcfuclH"
      },
      "source": [
        "### Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIhBcWwvuclH"
      },
      "source": [
        "def create_time_features(df):\n",
        "    df['month']= df.date.dt.month\n",
        "    df['year']= df.date.dt.year\n",
        "    df['day_of_month']= df.date.dt.day\n",
        "    df['day_of_week']= df.date.dt.dayofweek+1\n",
        "    df['day_of_year']= df.date.dt.dayofyear\n",
        "    df['week_of_year']=df.date.dt.weekofyear\n",
        "    df['is_weekend']= df.date.dt.weekday//4\n",
        "    df['is_startofmonth']= df.date.dt.is_month_start.astype(int)\n",
        "    df['is_endofmonth']= df.date.dt.is_month_end.astype(int)\n",
        "    return df\n",
        "train=create_time_features(train)\n",
        "data= create_time_features(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpcyVa9RuclH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6a9f0d5-fcf3-45ac-b7fe-475aaa5585a8"
      },
      "source": [
        "train.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 116006 entries, 0 to 116005\n",
            "Data columns (total 13 columns):\n",
            " #   Column           Non-Null Count   Dtype         \n",
            "---  ------           --------------   -----         \n",
            " 0   date             116006 non-null  datetime64[ns]\n",
            " 1   store            116006 non-null  int64         \n",
            " 2   item             116006 non-null  int64         \n",
            " 3   sales            116006 non-null  int64         \n",
            " 4   month            116006 non-null  int64         \n",
            " 5   year             116006 non-null  int64         \n",
            " 6   day_of_month     116006 non-null  int64         \n",
            " 7   day_of_week      116006 non-null  int64         \n",
            " 8   day_of_year      116006 non-null  int64         \n",
            " 9   week_of_year     116006 non-null  int64         \n",
            " 10  is_weekend       116006 non-null  int64         \n",
            " 11  is_startofmonth  116006 non-null  int64         \n",
            " 12  is_endofmonth    116006 non-null  int64         \n",
            "dtypes: datetime64[ns](1), int64(12)\n",
            "memory usage: 11.5 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u060BgPbuclH"
      },
      "source": [
        "##### Analysing the training data , We concluded that dates are ranging from 2013-01-01 to 2017-12-31. And for training data from 2013-01-01 to 2013-03-31. That is we are required to forecast sales of future 3 months."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DR-kUO6BuclI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "54d0e13a-9121-45dc-aa72-b244f5ab3e5c"
      },
      "source": [
        "train= train.set_index('date')\n",
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>store</th>\n",
              "      <th>item</th>\n",
              "      <th>sales</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>day_of_month</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>day_of_year</th>\n",
              "      <th>week_of_year</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>is_startofmonth</th>\n",
              "      <th>is_endofmonth</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-01-01</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-02</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-03</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-04</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-05</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            store  item  sales  ...  is_weekend  is_startofmonth  is_endofmonth\n",
              "date                            ...                                            \n",
              "2013-01-01      1     1     13  ...           0                1              0\n",
              "2013-01-02      1     1     11  ...           0                0              0\n",
              "2013-01-03      1     1     14  ...           0                0              0\n",
              "2013-01-04      1     1     13  ...           1                0              0\n",
              "2013-01-05      1     1     10  ...           1                0              0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GrHDz2i5uclI"
      },
      "source": [
        "## EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLU4xjoxuclI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "2ae6961d-f567-4bab-8656-28efc215eaac"
      },
      "source": [
        "sns.kdeplot(train['sales'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6ed903e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zc4qOpvuclJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "5c8295e1-13ac-4a98-c2e7-914781506dd3"
      },
      "source": [
        "plt.figure(figsize=(14,6))\n",
        "sns.kdeplot(train['sales'], hue=train['store'], palette='nipy_spectral_r')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6cb559cd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vqu1HbTnuclJ"
      },
      "source": [
        "#### Sales occur more b/w 25 and 50\n",
        "#### More sales occur at store 7 and least at store 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCHi0ryiuclJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "5264cbc4-a2bd-43ed-bb3c-6a20ffd34a0e"
      },
      "source": [
        "plt.figure(figsize=(14,6))\n",
        "plot_order = train.groupby('item')['sales'].sum().sort_values(ascending=False).index.values\n",
        "sns.boxplot(data=train, x='item', y='sales',order=plot_order, palette='viridis')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6c07dbc50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1lTXnFeuclK"
      },
      "source": [
        "#### More sales are from item 15 and less from item 5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9069X_pHuclK"
      },
      "source": [
        "#sales by year\n",
        "agg_year_item= pd.pivot_table(train, index='year', columns='item', values='sales',aggfunc=np.mean).values\n",
        "agg_year_store= pd.pivot_table(train, index='year', columns='store', values='sales',aggfunc=np.mean).values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6boMAwGguclK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "0dfe87a7-5eb0-41a7-b68a-b5aed7dc45b5"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.subplot(121)\n",
        "plt.plot(agg_year_item/agg_year_item.mean(0)[np.newaxis])\n",
        "plt.title('ITEM')\n",
        "plt.ylabel('Relative Sales')\n",
        "plt.xlabel('Year')\n",
        "plt.subplot(122)\n",
        "plt.plot(agg_year_store/agg_year_store.mean(0)[np.newaxis])\n",
        "plt.title('STORE')\n",
        "plt.ylabel('Relative Sales')\n",
        "plt.xlabel('Year')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-Tbz0v_uclL"
      },
      "source": [
        "#### Items and Stores grow over time similiarly"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uku_ILg1uclL"
      },
      "source": [
        "#sales by month\n",
        "agg_month_item= pd.pivot_table(train, index='month', columns='item', values='sales',aggfunc=np.mean).values\n",
        "agg_month_store= pd.pivot_table(train, index='month', columns='store', values='sales',aggfunc=np.mean).values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZai5BSpuclL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "bfb65ac6-b5e1-48fa-aef3-dfe4faa65220"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.subplot(121)\n",
        "plt.plot(agg_month_item/agg_month_item.mean(0)[np.newaxis])\n",
        "plt.title('ITEM')\n",
        "plt.ylabel('Relative Sales')\n",
        "plt.xlabel('Month')\n",
        "plt.subplot(122)\n",
        "plt.plot(agg_month_store/agg_month_store.mean(0)[np.newaxis])\n",
        "plt.title('STORE')\n",
        "plt.ylabel('Relative Sales')\n",
        "plt.xlabel('Month')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3CtN_xAFuclL"
      },
      "source": [
        "#### Similiar pattern for both item and store sales"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtKVTqG_uclM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "12381d97-cba5-4f6d-d23f-f3d761f04701"
      },
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "plt.plot(train['sales'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe6bec68510>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQ2V4n3GuclM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6618f863-e84b-4645-c37a-0018bf685311"
      },
      "source": [
        "# Resampling to Monthly wise\n",
        "train_monthly= train.resample('m').mean()\n",
        "train_monthly.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3a_OD12GuclM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "34bebb96-da9c-41f4-b618-976aae0dee12"
      },
      "source": [
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "result= seasonal_decompose(train_monthly['sales'], model='additive')\n",
        "result.plot();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5eGiOT48uclM"
      },
      "source": [
        "#### Seems Data is non-stationary\n",
        "#### Data has clear trend and seasonality.\n",
        "#### Residuals are decomposing randomly around 0, so the series is additive."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oNKYFBKKuclN"
      },
      "source": [
        "#### ----------------------------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5lyI5i8uclN"
      },
      "source": [
        "## Checking for stationarity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKRbcGZwuclN"
      },
      "source": [
        "### 1) Rolling Mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjcg7IwBuclN"
      },
      "source": [
        "def test_stationarity(timeseries):\n",
        "    #Determing rolling statistics\n",
        "    rolmean = timeseries.rolling(window=12).mean()\n",
        "    rolstd = timeseries.rolling(window=12).std()\n",
        "\n",
        "    #Plot rolling statistics:\n",
        "    fig = plt.figure(figsize=(16, 4))\n",
        "    orig = plt.plot(timeseries, color='blue',label='Original')\n",
        "    mean = plt.plot(rolmean, color='red', label='Rolling Mean')\n",
        "    std = plt.plot(rolstd, color='black', label = 'Rolling Std')\n",
        "    plt.legend(loc='best')\n",
        "    plt.title('Rolling Mean & Standard Deviation')\n",
        "    plt.show(block=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o12WnUVquclO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "897b31b4-21b4-44cc-c070-18d42c841f53"
      },
      "source": [
        "test_stationarity(train_monthly['sales'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AgmibHNtuclO"
      },
      "source": [
        "### 2) ADCF TEST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8tmmpJDLuclO"
      },
      "source": [
        "#H0-->null hypothesis-->It is non-stationary\n",
        "#H1-->Alternate hypothesis --> It is stationary\n",
        "def adfuller_test(timeseries):\n",
        "    results= adfuller(timeseries)\n",
        "    labels= ['ADF test statistic', 'p-value','Lags used', 'No.of observations used']\n",
        "    for value, label in zip(results, labels):\n",
        "        print(label+ ':' +str(value))\n",
        "    if results[1]<=0.01:\n",
        "        print('Strong evidence againt null hypothesis(H1). Its stationary')\n",
        "    else:\n",
        "        print('Weak evidence against alternate hypothesis(H1). Its non-stationary')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C0vVvDiwuclO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85ed8994-2347-475f-db80-952eabf4e631"
      },
      "source": [
        "adfuller_test(train_monthly['sales'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ADF test statistic:-5.736278143153922\n",
            "p-value:6.427360681442051e-07\n",
            "Lags used:11\n",
            "No.of observations used:48\n",
            "Strong evidence againt null hypothesis(H1). Its stationary\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iDWWvGhouclP"
      },
      "source": [
        "### Plotting ACF ANF PACF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXiT0xN4uclP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "66fb2a93-5d41-4389-dfc9-8dedcef47bb5"
      },
      "source": [
        "fig = plt.figure(figsize=(12,8))\n",
        "ax1 = fig.add_subplot(211)\n",
        "fig = sm.graphics.tsa.plot_acf(train_monthly['sales'].dropna(), lags=10, ax=ax1) # \n",
        "ax2 = fig.add_subplot(212)\n",
        "fig = sm.graphics.tsa.plot_pacf(train_monthly['sales'].dropna(), lags=10, ax=ax2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uhr97KkmuclP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fcbe0297-2e5f-4ba3-e61d-4f359705d294"
      },
      "source": [
        "train1= train_monthly[0:48]\n",
        "test1= train_monthly[48:]\n",
        "print('training data:', train1.shape[0])\n",
        "print('testing data:', test1.shape[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training data: 48\n",
            "testing data: 12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xu6USSMQuclP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "8b82b29b-c6c8-43d6-afdb-b6cf8167b117"
      },
      "source": [
        "result_ar= ARIMA(train1['sales'], order=(0,1,1)).fit(disp=False)\n",
        "result_ar.summary()"
      ],
      "execution_count": null,
      "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>47</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>          <td>ARIMA(0, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-139.886</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>4.745</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>          <td>Tue, 27 Apr 2021</td> <th>  AIC                </th>  <td>285.772</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>              <td>17:08:56</td>     <th>  BIC                </th>  <td>291.322</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>           <td>02-28-2013</td>    <th>  HQIC               </th>  <td>287.860</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                 <td>- 12-31-2016</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>    0.1691</td> <td>    0.795</td> <td>    0.213</td> <td> 0.833</td> <td>   -1.390</td> <td>    1.728</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1.D.sales</th> <td>    0.1514</td> <td>    0.130</td> <td>    1.163</td> <td> 0.251</td> <td>   -0.104</td> <td>    0.407</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>MA.1</th> <td>          -6.6053</td> <td>          +0.0000j</td> <td>           6.6053</td> <td>           0.5000</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:                   47\n",
              "Model:                 ARIMA(0, 1, 1)   Log Likelihood                -139.886\n",
              "Method:                       css-mle   S.D. of innovations              4.745\n",
              "Date:                Tue, 27 Apr 2021   AIC                            285.772\n",
              "Time:                        17:08:56   BIC                            291.322\n",
              "Sample:                    02-28-2013   HQIC                           287.860\n",
              "                         - 12-31-2016                                         \n",
              "=================================================================================\n",
              "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
              "---------------------------------------------------------------------------------\n",
              "const             0.1691      0.795      0.213      0.833      -1.390       1.728\n",
              "ma.L1.D.sales     0.1514      0.130      1.163      0.251      -0.104       0.407\n",
              "                                    Roots                                    \n",
              "=============================================================================\n",
              "                  Real          Imaginary           Modulus         Frequency\n",
              "-----------------------------------------------------------------------------\n",
              "MA.1           -6.6053           +0.0000j            6.6053            0.5000\n",
              "-----------------------------------------------------------------------------\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRaAX6mvuclQ"
      },
      "source": [
        "ar_pred= result_ar.predict(start=48, end=60).rename('AR forecasting')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5PcnZlwuclQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "79552ae6-da3a-4931-dba7-c351607f225b"
      },
      "source": [
        "train1['sales'].plot(figsize=(14,6), legend=True, color='orange')\n",
        "ar_pred.plot(figsize=(14,6), legend=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6bea0afd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4fvw3yAuclR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "outputId": "6d5a96de-f9fc-4dc9-cdf2-b3066390ea10"
      },
      "source": [
        "result_sarima= sm.tsa.statespace.SARIMAX(train1['sales'], order=(2,1,2), seasonal_order=(2,1,2,4)).fit()\n",
        "result_sarima.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/base/model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Statespace Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>               <td>sales</td>             <th>  No. Observations:  </th>   <td>48</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(2, 1, 2)x(2, 1, 2, 4)</td> <th>  Log Likelihood     </th> <td>-67.922</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                  <td>Tue, 27 Apr 2021</td>        <th>  AIC                </th> <td>153.845</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                      <td>17:08:58</td>            <th>  BIC                </th> <td>169.696</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                   <td>01-31-2013</td>           <th>  HQIC               </th> <td>159.690</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                         <td>- 12-31-2016</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>    0.2764</td> <td>    1.286</td> <td>    0.215</td> <td> 0.830</td> <td>   -2.243</td> <td>    2.796</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L2</th>   <td>    0.5141</td> <td>    1.047</td> <td>    0.491</td> <td> 0.623</td> <td>   -1.537</td> <td>    2.566</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1</th>   <td>   -0.2589</td> <td>    1.330</td> <td>   -0.195</td> <td> 0.846</td> <td>   -2.865</td> <td>    2.347</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L2</th>   <td>   -0.7159</td> <td>    1.139</td> <td>   -0.629</td> <td> 0.530</td> <td>   -2.948</td> <td>    1.516</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.S.L4</th> <td>   -1.0159</td> <td>    0.038</td> <td>  -26.410</td> <td> 0.000</td> <td>   -1.091</td> <td>   -0.941</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.S.L8</th> <td>   -0.9898</td> <td>    0.010</td> <td>  -96.266</td> <td> 0.000</td> <td>   -1.010</td> <td>   -0.970</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.S.L4</th> <td>    0.0066</td> <td>    0.424</td> <td>    0.016</td> <td> 0.988</td> <td>   -0.824</td> <td>    0.837</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.S.L8</th> <td>    0.0471</td> <td>    0.426</td> <td>    0.111</td> <td> 0.912</td> <td>   -0.788</td> <td>    0.882</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th>  <td>    0.6224</td> <td>    0.325</td> <td>    1.913</td> <td> 0.056</td> <td>   -0.015</td> <td>    1.260</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (Q):</th>          <td>34.81</td> <th>  Jarque-Bera (JB):  </th> <td>0.54</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.70</td>  <th>  Prob(JB):          </th> <td>0.76</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>1.62</td>  <th>  Skew:              </th> <td>-0.27</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.37</td>  <th>  Kurtosis:          </th> <td>3.05</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",
              "                                 Statespace Model Results                                \n",
              "=========================================================================================\n",
              "Dep. Variable:                             sales   No. Observations:                   48\n",
              "Model:             SARIMAX(2, 1, 2)x(2, 1, 2, 4)   Log Likelihood                 -67.922\n",
              "Date:                           Tue, 27 Apr 2021   AIC                            153.845\n",
              "Time:                                   17:08:58   BIC                            169.696\n",
              "Sample:                               01-31-2013   HQIC                           159.690\n",
              "                                    - 12-31-2016                                         \n",
              "Covariance Type:                             opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ar.L1          0.2764      1.286      0.215      0.830      -2.243       2.796\n",
              "ar.L2          0.5141      1.047      0.491      0.623      -1.537       2.566\n",
              "ma.L1         -0.2589      1.330     -0.195      0.846      -2.865       2.347\n",
              "ma.L2         -0.7159      1.139     -0.629      0.530      -2.948       1.516\n",
              "ar.S.L4       -1.0159      0.038    -26.410      0.000      -1.091      -0.941\n",
              "ar.S.L8       -0.9898      0.010    -96.266      0.000      -1.010      -0.970\n",
              "ma.S.L4        0.0066      0.424      0.016      0.988      -0.824       0.837\n",
              "ma.S.L8        0.0471      0.426      0.111      0.912      -0.788       0.882\n",
              "sigma2         0.6224      0.325      1.913      0.056      -0.015       1.260\n",
              "===================================================================================\n",
              "Ljung-Box (Q):                       34.81   Jarque-Bera (JB):                 0.54\n",
              "Prob(Q):                              0.70   Prob(JB):                         0.76\n",
              "Heteroskedasticity (H):               1.62   Skew:                            -0.27\n",
              "Prob(H) (two-sided):                  0.37   Kurtosis:                         3.05\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5_u3lGttuclR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "f87ec306-6908-40fb-b088-939de460f3db"
      },
      "source": [
        "sar_pred= result_sarima.predict(start=48, end=59).rename('SARIMAX forecasting')\n",
        "train1['sales'].plot(figsize=(14,6), legend=True, color='orange')\n",
        "sar_pred.plot(figsize=(14,6), legend=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6be885990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYVQxLVzuclR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "dbc4ceda-b480-4c8d-bbcc-d1a2b81d5684"
      },
      "source": [
        "# creating data for 24 months\n",
        "from pandas.tseries.offsets import DateOffset\n",
        "future_dates= [train_monthly.index[-1] + DateOffset(months=x) for x in range(0,24)]\n",
        "future_data= pd.DataFrame(index=future_dates[1:],columns=train_monthly.columns)\n",
        "data_concat= pd.concat([train_monthly, future_data])\n",
        "data_concat.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>store</th>\n",
              "      <th>item</th>\n",
              "      <th>sales</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>day_of_month</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>day_of_year</th>\n",
              "      <th>week_of_year</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>is_startofmonth</th>\n",
              "      <th>is_endofmonth</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-01-31</th>\n",
              "      <td>5.3125</td>\n",
              "      <td>3.71875</td>\n",
              "      <td>21.095262</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3.903226</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2.870968</td>\n",
              "      <td>0.387097</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.032258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-02-28</th>\n",
              "      <td>5.3125</td>\n",
              "      <td>3.71875</td>\n",
              "      <td>23.887277</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>14.5</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>45.5</td>\n",
              "      <td>7.071429</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.035714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-03-31</th>\n",
              "      <td>5.3125</td>\n",
              "      <td>3.71875</td>\n",
              "      <td>28.798387</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>4.193548</td>\n",
              "      <td>75.0</td>\n",
              "      <td>11.258065</td>\n",
              "      <td>0.483871</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.032258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-04-30</th>\n",
              "      <td>5.3125</td>\n",
              "      <td>3.71875</td>\n",
              "      <td>32.766667</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>15.5</td>\n",
              "      <td>3.833333</td>\n",
              "      <td>105.5</td>\n",
              "      <td>15.666667</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-05-31</th>\n",
              "      <td>5.3125</td>\n",
              "      <td>3.71875</td>\n",
              "      <td>35.595262</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>136.0</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.419355</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.032258</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             store     item  ...  is_startofmonth  is_endofmonth\n",
              "2013-01-31  5.3125  3.71875  ...         0.032258       0.032258\n",
              "2013-02-28  5.3125  3.71875  ...         0.035714       0.035714\n",
              "2013-03-31  5.3125  3.71875  ...         0.032258       0.032258\n",
              "2013-04-30  5.3125  3.71875  ...         0.033333       0.033333\n",
              "2013-05-31  5.3125  3.71875  ...         0.032258       0.032258\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6QCOuKPuclS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "cd474837-9735-45d0-db34-2c15d4bd1176"
      },
      "source": [
        "data_concat['forecast_sarima']=result_sarima.predict(start=60, end=72)\n",
        "data_concat[['sales','forecast_sarima']].plot(figsize=(14,8), color=['orange','blue'])\n",
        "plt.title('Sales for next 12 months[SARIMAX]')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Sales for next 12 months[SARIMAX]')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ywxOWfo0uclS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4484ce3-26f9-4b91-aecb-7ebdc8385b2e"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "error= mean_squared_error(test1.sales, sar_pred)\n",
        "error"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.7374438074610927"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "chzX2BKruclS"
      },
      "source": [
        "### SARIMAX is performing better than ARIMA model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zwm7AOaIuclT"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JDeOudjwuclT"
      },
      "source": [
        "## Using LGB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lw8VxE-QuclT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7eb55699-01e3-41aa-f2a7-d5c965e72d9d"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(161006, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sfPEPjxFuclT"
      },
      "source": [
        "# Sort the values per store, item and date so that values would be shifted equally\n",
        "data.sort_values(by=['store','item', 'date'], inplace=True, axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C5D6zpDuuclT"
      },
      "source": [
        "## Features creating from previous sales values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgR7lbxouclT"
      },
      "source": [
        "# Creating sales lag features\n",
        "def lag_features(dataframe, lags):\n",
        "    dataframe = dataframe.copy()\n",
        "    for lag in lags:\n",
        "        dataframe['sales_lag_' + str(lag)] = dataframe.groupby([\"store\", \"item\"])['sales'].transform(\n",
        "            lambda x: x.shift(lag)) + np.random.normal(size=(len(data),))\n",
        "    return dataframe"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yibe0KYRuclU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "6f6b0a37-040e-4b1c-8fc5-88499ae534d1"
      },
      "source": [
        "data= lag_features(data, lags=[91,98,105,112,119,126,182,364,546,728])\n",
        "data.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>store</th>\n",
              "      <th>item</th>\n",
              "      <th>sales</th>\n",
              "      <th>id</th>\n",
              "      <th>sales_lag_91</th>\n",
              "      <th>sales_lag_98</th>\n",
              "      <th>sales_lag_105</th>\n",
              "      <th>sales_lag_112</th>\n",
              "      <th>sales_lag_119</th>\n",
              "      <th>sales_lag_126</th>\n",
              "      <th>sales_lag_182</th>\n",
              "      <th>sales_lag_364</th>\n",
              "      <th>sales_lag_546</th>\n",
              "      <th>sales_lag_728</th>\n",
              "      <th>sales_roll_mean_365</th>\n",
              "      <th>sales_roll_mean_546</th>\n",
              "      <th>sales_roll_median_365</th>\n",
              "      <th>sales_roll_median_546</th>\n",
              "      <th>sales_ewm_alpha_095_lag_1</th>\n",
              "      <th>sales_ewm_alpha_09_lag_1</th>\n",
              "      <th>sales_ewm_alpha_08_lag_1</th>\n",
              "      <th>sales_ewm_alpha_07_lag_1</th>\n",
              "      <th>sales_ewm_alpha_05_lag_1</th>\n",
              "      <th>sales_ewm_alpha_095_lag_91</th>\n",
              "      <th>sales_ewm_alpha_095_lag_98</th>\n",
              "      <th>sales_ewm_alpha_095_lag_105</th>\n",
              "      <th>sales_ewm_alpha_095_lag_112</th>\n",
              "      <th>sales_ewm_alpha_095_lag_119</th>\n",
              "      <th>sales_ewm_alpha_095_lag_126</th>\n",
              "      <th>sales_ewm_alpha_095_lag_182</th>\n",
              "      <th>sales_ewm_alpha_095_lag_364</th>\n",
              "      <th>sales_ewm_alpha_095_lag_546</th>\n",
              "      <th>sales_ewm_alpha_095_lag_728</th>\n",
              "      <th>sales_ewm_alpha_09_lag_91</th>\n",
              "      <th>sales_ewm_alpha_09_lag_98</th>\n",
              "      <th>sales_ewm_alpha_09_lag_105</th>\n",
              "      <th>sales_ewm_alpha_09_lag_112</th>\n",
              "      <th>sales_ewm_alpha_09_lag_119</th>\n",
              "      <th>sales_ewm_alpha_09_lag_126</th>\n",
              "      <th>sales_ewm_alpha_09_lag_182</th>\n",
              "      <th>sales_ewm_alpha_09_lag_364</th>\n",
              "      <th>sales_ewm_alpha_09_lag_546</th>\n",
              "      <th>sales_ewm_alpha_09_lag_728</th>\n",
              "      <th>sales_ewm_alpha_08_lag_91</th>\n",
              "      <th>sales_ewm_alpha_08_lag_98</th>\n",
              "      <th>sales_ewm_alpha_08_lag_105</th>\n",
              "      <th>sales_ewm_alpha_08_lag_112</th>\n",
              "      <th>sales_ewm_alpha_08_lag_119</th>\n",
              "      <th>sales_ewm_alpha_08_lag_126</th>\n",
              "      <th>sales_ewm_alpha_08_lag_182</th>\n",
              "      <th>sales_ewm_alpha_08_lag_364</th>\n",
              "      <th>sales_ewm_alpha_08_lag_546</th>\n",
              "      <th>sales_ewm_alpha_08_lag_728</th>\n",
              "      <th>sales_ewm_alpha_07_lag_91</th>\n",
              "      <th>sales_ewm_alpha_07_lag_98</th>\n",
              "      <th>sales_ewm_alpha_07_lag_105</th>\n",
              "      <th>sales_ewm_alpha_07_lag_112</th>\n",
              "      <th>sales_ewm_alpha_07_lag_119</th>\n",
              "      <th>sales_ewm_alpha_07_lag_126</th>\n",
              "      <th>sales_ewm_alpha_07_lag_182</th>\n",
              "      <th>sales_ewm_alpha_07_lag_364</th>\n",
              "      <th>sales_ewm_alpha_07_lag_546</th>\n",
              "      <th>sales_ewm_alpha_07_lag_728</th>\n",
              "      <th>sales_ewm_alpha_05_lag_91</th>\n",
              "      <th>sales_ewm_alpha_05_lag_98</th>\n",
              "      <th>sales_ewm_alpha_05_lag_105</th>\n",
              "      <th>sales_ewm_alpha_05_lag_112</th>\n",
              "      <th>sales_ewm_alpha_05_lag_119</th>\n",
              "      <th>sales_ewm_alpha_05_lag_126</th>\n",
              "      <th>sales_ewm_alpha_05_lag_182</th>\n",
              "      <th>sales_ewm_alpha_05_lag_364</th>\n",
              "      <th>sales_ewm_alpha_05_lag_546</th>\n",
              "      <th>sales_ewm_alpha_05_lag_728</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>44995</th>\n",
              "      <td>2018-03-27</td>\n",
              "      <td>10</td>\n",
              "      <td>50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44995.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44996</th>\n",
              "      <td>2018-03-28</td>\n",
              "      <td>10</td>\n",
              "      <td>50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44996.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44997</th>\n",
              "      <td>2018-03-29</td>\n",
              "      <td>10</td>\n",
              "      <td>50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44997.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44998</th>\n",
              "      <td>2018-03-30</td>\n",
              "      <td>10</td>\n",
              "      <td>50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44998.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44999</th>\n",
              "      <td>2018-03-31</td>\n",
              "      <td>10</td>\n",
              "      <td>50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44999.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\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": [
              "            date  store  ...  sales_ewm_alpha_05_lag_546  sales_ewm_alpha_05_lag_728\n",
              "44995 2018-03-27     10  ...                         NaN                         NaN\n",
              "44996 2018-03-28     10  ...                         NaN                         NaN\n",
              "44997 2018-03-29     10  ...                         NaN                         NaN\n",
              "44998 2018-03-30     10  ...                         NaN                         NaN\n",
              "44999 2018-03-31     10  ...                         NaN                         NaN\n",
              "\n",
              "[5 rows x 74 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RvujtKH0uclU"
      },
      "source": [
        "In order to find out possible seasonalities, I will be creating moving averages for specified time intervals. In order to eliminate today's affect on moving average values, I will take 1 shift"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Myy26ybyuclU"
      },
      "source": [
        "# Creating sales rolling mean features\n",
        "def roll_mean_features(df, windows):\n",
        "    df = df.copy()\n",
        "    for window in windows:\n",
        "        df['sales_roll_mean_' + str(window)] = df.groupby([\"store\", \"item\"])['sales'].transform(\n",
        "            lambda x: x.shift(1).rolling(window=window, min_periods=10, win_type=None).mean()) \n",
        "        +np.random.normal(size=(len(data),))\n",
        "    return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vNl7z6ybuclU"
      },
      "source": [
        "data= roll_mean_features(data, windows=[365,546])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ji4mgo0juclV"
      },
      "source": [
        "# Creating sales rolling median features\n",
        "def roll_median_features(df, windows):\n",
        "    df = df.copy()\n",
        "    for window in windows:\n",
        "        df['sales_roll_median_' + str(window)] = df.groupby([\"store\", \"item\"])['sales'].transform(\n",
        "            lambda x: x.shift(1).rolling(window=window, min_periods=10, win_type=None).median())\n",
        "        +np.random.normal(size=(len(data),))\n",
        "    return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qneyiI-VuclV"
      },
      "source": [
        "data= roll_median_features(data,windows=[365,546])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hnhtAmsuclV"
      },
      "source": [
        "The value in time t highly depends on the value in time t-1, so in order to have a better prediction, while computing the average value, the values should not be equally weighted."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLy6vwfIuclV"
      },
      "source": [
        "# creating sales exponentially weighed mean features\n",
        "def ewm_features(dataframe, alphas, lags):\n",
        "    dataframe = dataframe.copy()\n",
        "    for alpha in alphas:\n",
        "        for lag in lags:\n",
        "            dataframe['sales_ewm_alpha_' + str(alpha).replace(\".\", \"\") + \"_lag_\" + str(lag)] = \\\n",
        "                dataframe.groupby([\"store\", \"item\"])['sales']. \\\n",
        "                    transform(lambda x: x.shift(lag).ewm(alpha=alpha).mean())\n",
        "    return dataframe"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4RynpOLuclW"
      },
      "source": [
        "data= ewm_features(data, alphas = [0.95, 0.9, 0.8, 0.7, 0.5], lags=[91,98,105,112,119,126,182,364,546,728])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXgKB6sTuclW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c2c5c04-409d-4991-a5da-f8a27bae5482"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(161006, 74)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "5Qa6Wq4lRxSk",
        "outputId": "7c57d735-3e8a-4ce8-dc27-96bdfcc535dd"
      },
      "source": [
        "data.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>store</th>\n",
              "      <th>item</th>\n",
              "      <th>sales</th>\n",
              "      <th>id</th>\n",
              "      <th>sales_lag_91</th>\n",
              "      <th>sales_lag_98</th>\n",
              "      <th>sales_lag_105</th>\n",
              "      <th>sales_lag_112</th>\n",
              "      <th>sales_lag_119</th>\n",
              "      <th>sales_lag_126</th>\n",
              "      <th>sales_lag_182</th>\n",
              "      <th>sales_lag_364</th>\n",
              "      <th>sales_lag_546</th>\n",
              "      <th>sales_lag_728</th>\n",
              "      <th>sales_roll_mean_365</th>\n",
              "      <th>sales_roll_mean_546</th>\n",
              "      <th>sales_roll_median_365</th>\n",
              "      <th>sales_roll_median_546</th>\n",
              "      <th>sales_ewm_alpha_095_lag_1</th>\n",
              "      <th>sales_ewm_alpha_09_lag_1</th>\n",
              "      <th>sales_ewm_alpha_08_lag_1</th>\n",
              "      <th>sales_ewm_alpha_07_lag_1</th>\n",
              "      <th>sales_ewm_alpha_05_lag_1</th>\n",
              "      <th>sales_ewm_alpha_095_lag_91</th>\n",
              "      <th>sales_ewm_alpha_095_lag_98</th>\n",
              "      <th>sales_ewm_alpha_095_lag_105</th>\n",
              "      <th>sales_ewm_alpha_095_lag_112</th>\n",
              "      <th>sales_ewm_alpha_095_lag_119</th>\n",
              "      <th>sales_ewm_alpha_095_lag_126</th>\n",
              "      <th>sales_ewm_alpha_095_lag_182</th>\n",
              "      <th>sales_ewm_alpha_095_lag_364</th>\n",
              "      <th>sales_ewm_alpha_095_lag_546</th>\n",
              "      <th>sales_ewm_alpha_095_lag_728</th>\n",
              "      <th>sales_ewm_alpha_09_lag_91</th>\n",
              "      <th>sales_ewm_alpha_09_lag_98</th>\n",
              "      <th>sales_ewm_alpha_09_lag_105</th>\n",
              "      <th>sales_ewm_alpha_09_lag_112</th>\n",
              "      <th>sales_ewm_alpha_09_lag_119</th>\n",
              "      <th>sales_ewm_alpha_09_lag_126</th>\n",
              "      <th>sales_ewm_alpha_09_lag_182</th>\n",
              "      <th>sales_ewm_alpha_09_lag_364</th>\n",
              "      <th>sales_ewm_alpha_09_lag_546</th>\n",
              "      <th>sales_ewm_alpha_09_lag_728</th>\n",
              "      <th>sales_ewm_alpha_08_lag_91</th>\n",
              "      <th>sales_ewm_alpha_08_lag_98</th>\n",
              "      <th>sales_ewm_alpha_08_lag_105</th>\n",
              "      <th>sales_ewm_alpha_08_lag_112</th>\n",
              "      <th>sales_ewm_alpha_08_lag_119</th>\n",
              "      <th>sales_ewm_alpha_08_lag_126</th>\n",
              "      <th>sales_ewm_alpha_08_lag_182</th>\n",
              "      <th>sales_ewm_alpha_08_lag_364</th>\n",
              "      <th>sales_ewm_alpha_08_lag_546</th>\n",
              "      <th>sales_ewm_alpha_08_lag_728</th>\n",
              "      <th>sales_ewm_alpha_07_lag_91</th>\n",
              "      <th>sales_ewm_alpha_07_lag_98</th>\n",
              "      <th>sales_ewm_alpha_07_lag_105</th>\n",
              "      <th>sales_ewm_alpha_07_lag_112</th>\n",
              "      <th>sales_ewm_alpha_07_lag_119</th>\n",
              "      <th>sales_ewm_alpha_07_lag_126</th>\n",
              "      <th>sales_ewm_alpha_07_lag_182</th>\n",
              "      <th>sales_ewm_alpha_07_lag_364</th>\n",
              "      <th>sales_ewm_alpha_07_lag_546</th>\n",
              "      <th>sales_ewm_alpha_07_lag_728</th>\n",
              "      <th>sales_ewm_alpha_05_lag_91</th>\n",
              "      <th>sales_ewm_alpha_05_lag_98</th>\n",
              "      <th>sales_ewm_alpha_05_lag_105</th>\n",
              "      <th>sales_ewm_alpha_05_lag_112</th>\n",
              "      <th>sales_ewm_alpha_05_lag_119</th>\n",
              "      <th>sales_ewm_alpha_05_lag_126</th>\n",
              "      <th>sales_ewm_alpha_05_lag_182</th>\n",
              "      <th>sales_ewm_alpha_05_lag_364</th>\n",
              "      <th>sales_ewm_alpha_05_lag_546</th>\n",
              "      <th>sales_ewm_alpha_05_lag_728</th>\n",
              "    </tr>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            date  store  ...  sales_ewm_alpha_05_lag_546  sales_ewm_alpha_05_lag_728\n",
              "44995 2018-03-27     10  ...                         NaN                         NaN\n",
              "44996 2018-03-28     10  ...                         NaN                         NaN\n",
              "44997 2018-03-29     10  ...                         NaN                         NaN\n",
              "44998 2018-03-30     10  ...                         NaN                         NaN\n",
              "44999 2018-03-31     10  ...                         NaN                         NaN\n",
              "\n",
              "[5 rows x 74 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1SbMdhHouclW"
      },
      "source": [
        "### One Hot Encoding Some Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKd2KnCsuclW"
      },
      "source": [
        "data= pd.get_dummies(data, columns=['item','store','month','day_of_week'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HuRySMAduclX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09202428-8bf3-4b26-d5f2-d2fe91ea3b1b"
      },
      "source": [
        "# log tranformation of sales to reduce no.of iterations and there by computation time  \n",
        "data['sales']= np.log1p(data['sales'].values)\n",
        "data['sales'].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2.639057\n",
              "1    2.484907\n",
              "2    2.708050\n",
              "3    2.639057\n",
              "4    2.397895\n",
              "Name: sales, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqV_K1LmuclX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eLKSViskuclX"
      },
      "source": [
        "### Train - Validation Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VK7TG0qZuclX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0da0741-0635-44e1-9d6c-bbe15339a273"
      },
      "source": [
        "#training\n",
        "train = data.loc[(data[\"date\"] < \"2017-01-01\"), :]\n",
        "print(train['date'].min()), print(train['date'].max())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2013-01-01 00:00:00\n",
            "2016-12-31 00:00:00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6U2kWutuclY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ad98d7f-4807-4671-f2de-afe0696e0b1f"
      },
      "source": [
        "#validation\n",
        "val = data.loc[(data[\"date\"] >= \"2017-01-01\") & (data[\"date\"] < \"2017-04-01\"), :]\n",
        "print(val['date'].min()), print(val['date'].max())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2017-01-01 00:00:00\n",
            "2017-03-31 00:00:00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3Gj33bpuclY"
      },
      "source": [
        "### Custom Cost Function\n",
        "custom cost function which is based on SMAPE which reverses the log values and calculates the SMAPE."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pKPRP3oluclY"
      },
      "source": [
        "def smape(preds, target):\n",
        "    n = len(preds)\n",
        "    masked_arr = ~((preds == 0) & (target == 0))\n",
        "    preds, target = preds[masked_arr], target[masked_arr]\n",
        "    num = np.abs(preds-target)\n",
        "    denom = np.abs(preds)+np.abs(target)\n",
        "    smape_val = (200*np.sum(num/denom))/n\n",
        "    return smape_val\n",
        "\n",
        "def lgbm_smape(preds, train_data):\n",
        "    labels = train_data.get_label()\n",
        "    smape_val = smape(np.expm1(preds), np.expm1(labels))\n",
        "    return 'SMAPE', smape_val, False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4PF2TRouclY"
      },
      "source": [
        "cols = [col for col in train.columns if col not in ['date', 'id', \"sales\", \"year\"]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8wgKXGAuclZ"
      },
      "source": [
        "X_train= train[cols]\n",
        "y_train= train['sales']\n",
        "X_val= val[cols]\n",
        "y_val= val['sales']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYZv-48vuclZ"
      },
      "source": [
        "## Building LGB MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zlh0VTs_uclZ"
      },
      "source": [
        "import lightgbm as lgb"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bEWua_gucla"
      },
      "source": [
        "lgb_params = {'metric': {'mae'},\n",
        "              'num_leaves': 10,\n",
        "              'learning_rate': 0.02,\n",
        "              'feature_fraction': 0.8,\n",
        "              'max_depth': 5,\n",
        "              'verbose': 0,\n",
        "              'num_boost_round': 15000,\n",
        "              'early_stopping_rounds': 200,\n",
        "              'nthread': -1}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKL2z4S6A_KD"
      },
      "source": [
        "lgbtrain = lgb.Dataset(data=X_train, label=y_train, feature_name=cols)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ml1zsTj-BAus"
      },
      "source": [
        "lgbval = lgb.Dataset(data=X_val, label=y_val, reference=lgbtrain, feature_name=cols)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZjpffX1fBF6L",
        "outputId": "6aee1e8b-0c74-4b32-a774-e25905b99312"
      },
      "source": [
        "model = lgb.train(lgb_params, lgbtrain,\n",
        "                  valid_sets=[lgbtrain, lgbval],\n",
        "                  num_boost_round=lgb_params['num_boost_round'],\n",
        "                  early_stopping_rounds=lgb_params['early_stopping_rounds'],\n",
        "                  feval=lgbm_smape,\n",
        "                  verbose_eval=200)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training until validation scores don't improve for 200 rounds.\n",
            "[200]\ttraining's l1: 0.160313\ttraining's SMAPE: 16.6128\tvalid_1's l1: 0.166433\tvalid_1's SMAPE: 17.2769\n",
            "[400]\ttraining's l1: 0.15493\ttraining's SMAPE: 16.0703\tvalid_1's l1: 0.162086\tvalid_1's SMAPE: 16.8396\n",
            "[600]\ttraining's l1: 0.152652\ttraining's SMAPE: 15.8398\tvalid_1's l1: 0.160039\tvalid_1's SMAPE: 16.6318\n",
            "[800]\ttraining's l1: 0.151124\ttraining's SMAPE: 15.6851\tvalid_1's l1: 0.158904\tvalid_1's SMAPE: 16.5165\n",
            "[1000]\ttraining's l1: 0.150024\ttraining's SMAPE: 15.5736\tvalid_1's l1: 0.158117\tvalid_1's SMAPE: 16.4368\n",
            "[1200]\ttraining's l1: 0.149193\ttraining's SMAPE: 15.4893\tvalid_1's l1: 0.157614\tvalid_1's SMAPE: 16.386\n",
            "[1400]\ttraining's l1: 0.14851\ttraining's SMAPE: 15.42\tvalid_1's l1: 0.157269\tvalid_1's SMAPE: 16.3512\n",
            "[1600]\ttraining's l1: 0.147918\ttraining's SMAPE: 15.3598\tvalid_1's l1: 0.156934\tvalid_1's SMAPE: 16.3173\n",
            "[1800]\ttraining's l1: 0.14739\ttraining's SMAPE: 15.306\tvalid_1's l1: 0.156775\tvalid_1's SMAPE: 16.3013\n",
            "[2000]\ttraining's l1: 0.146911\ttraining's SMAPE: 15.2571\tvalid_1's l1: 0.156562\tvalid_1's SMAPE: 16.2795\n",
            "[2200]\ttraining's l1: 0.146467\ttraining's SMAPE: 15.2118\tvalid_1's l1: 0.156373\tvalid_1's SMAPE: 16.2604\n",
            "[2400]\ttraining's l1: 0.146048\ttraining's SMAPE: 15.169\tvalid_1's l1: 0.156227\tvalid_1's SMAPE: 16.2457\n",
            "[2600]\ttraining's l1: 0.14564\ttraining's SMAPE: 15.1272\tvalid_1's l1: 0.15608\tvalid_1's SMAPE: 16.2308\n",
            "[2800]\ttraining's l1: 0.145248\ttraining's SMAPE: 15.0871\tvalid_1's l1: 0.156043\tvalid_1's SMAPE: 16.2273\n",
            "[3000]\ttraining's l1: 0.14486\ttraining's SMAPE: 15.0474\tvalid_1's l1: 0.155971\tvalid_1's SMAPE: 16.2202\n",
            "[3200]\ttraining's l1: 0.144482\ttraining's SMAPE: 15.0086\tvalid_1's l1: 0.155944\tvalid_1's SMAPE: 16.2175\n",
            "[3400]\ttraining's l1: 0.14412\ttraining's SMAPE: 14.9714\tvalid_1's l1: 0.155917\tvalid_1's SMAPE: 16.2149\n",
            "[3600]\ttraining's l1: 0.143779\ttraining's SMAPE: 14.9365\tvalid_1's l1: 0.155886\tvalid_1's SMAPE: 16.2121\n",
            "[3800]\ttraining's l1: 0.14343\ttraining's SMAPE: 14.9007\tvalid_1's l1: 0.155799\tvalid_1's SMAPE: 16.2033\n",
            "[4000]\ttraining's l1: 0.143092\ttraining's SMAPE: 14.8661\tvalid_1's l1: 0.155768\tvalid_1's SMAPE: 16.2002\n",
            "[4200]\ttraining's l1: 0.142764\ttraining's SMAPE: 14.8323\tvalid_1's l1: 0.155734\tvalid_1's SMAPE: 16.1967\n",
            "[4400]\ttraining's l1: 0.142431\ttraining's SMAPE: 14.7981\tvalid_1's l1: 0.155743\tvalid_1's SMAPE: 16.1978\n",
            "Early stopping, best iteration is:\n",
            "[4243]\ttraining's l1: 0.142693\ttraining's SMAPE: 14.825\tvalid_1's l1: 0.155721\tvalid_1's SMAPE: 16.1954\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GAQ0dYMFHsGE",
        "outputId": "9d6994d6-a299-4ad3-d08f-33f439811b2d"
      },
      "source": [
        "y_pred_val= model.predict(X_val)\n",
        "y_pred_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.02621962, 2.57991177, 2.72727396, ..., 4.15314862, 4.22172385,\n",
              "       4.31536991])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ipKvW8rK9eS",
        "outputId": "53054cdb-607e-4e8e-9436-e8f478af2b6a"
      },
      "source": [
        "smape(np.expm1(y_pred_val), np.expm1(y_val))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16.19536171725934"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2YFitnjLgs5",
        "outputId": "76d0aa6d-5d8b-48e9-b360-520f35fe228a"
      },
      "source": [
        "model.best_iteration"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4243"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1d22A72MUcc",
        "outputId": "54f5073d-a104-4bb8-ac10-8afac5627943"
      },
      "source": [
        "model.best_score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "defaultdict(dict,\n",
              "            {'training': {'SMAPE': 14.825027929665477,\n",
              "              'l1': 0.14269275288811825},\n",
              "             'valid_1': {'SMAPE': 16.19536171725934,\n",
              "              'l1': 0.15572117089964624}})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jljZjJewNjCF"
      },
      "source": [
        "**Feature Importance**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "3rA5cqoFNemP",
        "outputId": "225e686e-2180-4ceb-94a0-840ec71bf327"
      },
      "source": [
        "# this one is the built-in plot function of LightGBM library\n",
        "\n",
        "lgb.plot_importance(model, max_num_features=20, figsize=(16, 10), importance_type=\"gain\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NtSAl7GcMXeY",
        "outputId": "378c9495-0dfa-408f-bc4d-6854cdd24ceb"
      },
      "source": [
        "y_pred_val= np.expm1(y_pred_val)\n",
        "y_pred_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([19.61913685, 12.19597386, 14.29114585, ..., 62.63404474,\n",
              "       67.15086479, 73.84130284])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqunLkz8MhkX",
        "outputId": "c656d74b-d9aa-4ad0-fb28-d806233a87d1"
      },
      "source": [
        "y_val= np.expm1(y_val)\n",
        "y_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1461      19.0\n",
              "1462      15.0\n",
              "1463      10.0\n",
              "1464      16.0\n",
              "1465      14.0\n",
              "          ... \n",
              "109280    55.0\n",
              "109281    72.0\n",
              "109282    69.0\n",
              "109283    80.0\n",
              "109284    68.0\n",
              "Name: sales, Length: 5670, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fUzCjx67M3eb",
        "outputId": "5cc9914e-d201-476a-cb39-63909629c679"
      },
      "source": [
        "y_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1461      19.0\n",
              "1462      15.0\n",
              "1463      10.0\n",
              "1464      16.0\n",
              "1465      14.0\n",
              "          ... \n",
              "109280    55.0\n",
              "109281    72.0\n",
              "109282    69.0\n",
              "109283    80.0\n",
              "109284    68.0\n",
              "Name: sales, Length: 5670, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O5mlcRdhPPlE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}