Store2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Store.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPiSI+C0J00X/2D0ZmA4LCv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ogut77/DataScience/blob/main/Store2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install --upgrade --no-deps statsmodels"
      ],
      "metadata": {
        "id": "BehTpmr16vni",
        "outputId": "ad15f1e9-8321-435f-8dd3-393b74f10693",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.7/dist-packages (0.10.2)\n",
            "Collecting statsmodels\n",
            "  Downloading statsmodels-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.8 MB 18.3 MB/s \n",
            "\u001b[?25hInstalling collected packages: statsmodels\n",
            "  Attempting uninstall: statsmodels\n",
            "    Found existing installation: statsmodels 0.10.2\n",
            "    Uninstalling statsmodels-0.10.2:\n",
            "      Successfully uninstalled statsmodels-0.10.2\n",
            "Successfully installed statsmodels-0.13.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "statsmodels"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "fB4ZqltwrpQn"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/ogut77/DataScience/main/data/kaggle_sales.csv')\n",
        "df = df[(df['store'] == 1) & (df['item'] == 1)] # item 1 in store 1\n",
        "df = df.set_index('date')\n",
        "\n",
        "y = pd.DataFrame(df['sales'])\n",
        "y.index.freq='D'\n",
        "train = y[:-365]\n",
        "test = y[-365:] # last year for testing\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test"
      ],
      "metadata": {
        "id": "wv-F186x7v2i",
        "outputId": "0cb3555b-7d07-4ea1-96e0-412357e8de51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            sales\n",
              "date             \n",
              "1/1/2017       19\n",
              "1/2/2017       15\n",
              "1/3/2017       10\n",
              "1/4/2017       16\n",
              "1/5/2017       14\n",
              "...           ...\n",
              "12/27/2017     14\n",
              "12/28/2017     19\n",
              "12/29/2017     15\n",
              "12/30/2017     27\n",
              "12/31/2017     23\n",
              "\n",
              "[365 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bfe7d7a5-9931-4fbe-b158-1d87241883cb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1/1/2017</th>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/2/2017</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/3/2017</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/4/2017</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/5/2017</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12/27/2017</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12/28/2017</th>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12/29/2017</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12/30/2017</th>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12/31/2017</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>365 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bfe7d7a5-9931-4fbe-b158-1d87241883cb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bfe7d7a5-9931-4fbe-b158-1d87241883cb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bfe7d7a5-9931-4fbe-b158-1d87241883cb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.xticks( rotation='vertical')\n",
        "plt.plot(train.index,train, label='TRAIN')\n",
        "plt.plot(test.index,test, label='TEST')\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "oh5xB6iFwnnZ",
        "outputId": "fef8fae3-cd0d-4eee-a73a-bb9e0d952077"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tbats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJbMN9Qk02IG",
        "outputId": "f4697db5-06f2-4469-ca66-d9f363e19d9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tbats\n",
            "  Downloading tbats-1.1.0-py3-none-any.whl (43 kB)\n",
            "\u001b[?25l\r\u001b[K     |███████▌                        | 10 kB 17.4 MB/s eta 0:00:01\r\u001b[K     |███████████████                 | 20 kB 8.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▍         | 30 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 40 kB 4.5 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 43 kB 920 kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from tbats) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from tbats) (1.0.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from tbats) (1.4.1)\n",
            "Collecting pmdarima\n",
            "  Downloading pmdarima-1.8.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (1.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.4 MB 6.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (1.24.3)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (1.3.5)\n",
            "Collecting statsmodels!=0.12.0,>=0.11\n",
            "  Downloading statsmodels-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.8 MB 46.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (1.1.0)\n",
            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (57.4.0)\n",
            "Requirement already satisfied: Cython!=0.29.18,>=0.29 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (0.29.28)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima->tbats) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima->tbats) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19->pmdarima->tbats) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->tbats) (3.1.0)\n",
            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.7/dist-packages (from statsmodels!=0.12.0,>=0.11->pmdarima->tbats) (0.5.2)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.7/dist-packages (from statsmodels!=0.12.0,>=0.11->pmdarima->tbats) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=21.3->statsmodels!=0.12.0,>=0.11->pmdarima->tbats) (3.0.8)\n",
            "Installing collected packages: statsmodels, pmdarima, tbats\n",
            "  Attempting uninstall: statsmodels\n",
            "    Found existing installation: statsmodels 0.10.2\n",
            "    Uninstalling statsmodels-0.10.2:\n",
            "      Successfully uninstalled statsmodels-0.10.2\n",
            "Successfully installed pmdarima-1.8.5 statsmodels-0.13.2 tbats-1.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tbats import TBATS, BATS# Fit the model\n",
        "estimator = TBATS(seasonal_periods=(7, 365.25))\n",
        "model = estimator.fit(train)# Forecast 365 days ahead\n",
        "y_forecast = model.forecast(steps=365)"
      ],
      "metadata": {
        "id": "xZ6a0h9Wyvbu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_forecast = model.forecast(steps=365)\n",
        "\n",
        "test_predictions=pd.DataFrame(y_forecast)\n",
        "test_predictions.columns=['sales']\n"
      ],
      "metadata": {
        "id": "N_lgFDvzvP2s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "print(f'Mean Absolute Error = {mean_absolute_error(test,y_forecast)}')\n",
        "print(f'Mean Squared Error = {mean_squared_error(test,y_forecast)}')\n",
        "import numpy as np\n",
        "def mape(actual,pred):\n",
        "    return np.mean(np.abs((actual - pred) / actual)) * 100  \n",
        "print(f'Mean Absolute Percentage Error = {mape(np.array(test),np.array(y_forecast) )}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LBUaYkgvxBpN",
        "outputId": "32860ec5-42c2-4f14-8ad9-9199a2f63f79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error = 3.8577289832270028\n",
            "Mean Squared Error = 23.637445028684905\n",
            "Mean Absolute Percentage Error = 35.497625473230976\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.plot(train,label='TRAIN')\n",
        "plt.plot(test,label='TEST')\n",
        "ni=test.index\n",
        "test_predictions=test_predictions.set_index(ni)\n",
        "plt.plot(test_predictions,label='FORECAST')\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.title('Train, Test and Predicted Test using TBATS ')"
      ],
      "metadata": {
        "id": "J7n-lOa3sSd0",
        "outputId": "0803c06b-36da-47e3-d85f-6ff9ca50515d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Train, Test and Predicted Test using TBATS ')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Holt Winters"
      ],
      "metadata": {
        "id": "9-KwA9wexUIf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
        "fitted_model = ExponentialSmoothing(train,trend='mul',seasonal='mul',seasonal_periods=365).fit()\n",
        "test_predictions = fitted_model.forecast(365).rename('sales')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rLkrgaR4xW9o",
        "outputId": "7de34cb3-8df9-428c-9a79-687749039503"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
            "  self._init_dates(dates, freq)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate forecasting Performance\n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "print(f'Mean Absolute Error = {mean_absolute_error(test,test_predictions)}')\n",
        "print(f'Mean Squared Error = {mean_squared_error(test,test_predictions)}')\n",
        "import numpy as np\n",
        "def mape(actual,pred):\n",
        "    return np.mean(np.abs((actual - pred) / actual)) * 100  \n",
        "print(f'Mean Absolute Percentage Error = {mape(np.array(test),np.array(test_predictions) )}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oM6NSczGzpXi",
        "outputId": "016583d0-89a1-4b5a-f090-6d3ce0a0422d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error = 5.859391287582682\n",
            "Mean Squared Error = 56.80871732224168\n",
            "Mean Absolute Percentage Error = 41.26774435015866\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(train,label='TRAIN')\n",
        "plt.plot(test,label='TEST')\n",
        "test_predictions=pd.DataFrame(test_predictions)\n",
        "ni=test.index\n",
        "test_predictions=test_predictions.set_index(ni)\n",
        "plt.plot(test_predictions,label='PREDICTION')\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.title('Train, Test and Predicted Test using Holt Winters')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "-8YNPFQh75iv",
        "outputId": "79facc24-8586-408e-dd86-7d72508959b8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Train, Test and Predicted Test using Holt Winters')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ETS"
      ],
      "metadata": {
        "id": "pqbphmJN6Acr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Damped-False"
      ],
      "metadata": {
        "id": "hxm2-uIgBmZu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.exponential_smoothing.ets import ETSModel\n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "test_predictions=ETSModel(train['sales'],error='add', trend='add',seasonal='add',damped_trend=False, seasonal_periods=365).fit().forecast(365)\n",
        "print(f'Mean Absolute Error = {mean_absolute_error(test,test_predictions)}')\n",
        "mse=mean_squared_error(test,test_predictions)\n",
        "rmse=np.sqrt(mse)\n",
        "print(f'Mean Squared Error = {mse}')\n",
        "print(f'Root Mean Squared Error = {rmse}')\n",
        "import numpy as np\n",
        "def mape(actual,pred):\n",
        "            return np.mean(np.abs((actual - pred) / actual)) * 100  \n",
        "print(f'Mean Absolute Percentage Error = {mape(np.array(test),np.array(test_predictions) )}')"
      ],
      "metadata": {
        "id": "yuZCzFy06EAp",
        "outputId": "843c2376-4fab-43b7-cdd5-d7d4ba5ef291",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/base/model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  ConvergenceWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error = 5.739792382732128\n",
            "Mean Squared Error = 52.05230966552013\n",
            "Root Mean Squared Error = 7.21472866194704\n",
            "Mean Absolute Percentage Error = 40.65177417815756\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Damped-True"
      ],
      "metadata": {
        "id": "m1DdwkyPBt3z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.exponential_smoothing.ets import ETSModel\n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "test_predictions=ETSModel(train['sales'],error='add', trend='add',seasonal='add',damped_trend=True, seasonal_periods=365).fit().forecast(365)\n",
        "print(f'Mean Absolute Error = {mean_absolute_error(test,test_predictions)}')\n",
        "mse=mean_squared_error(test,test_predictions)\n",
        "rmse=np.sqrt(mse)\n",
        "print(f'Mean Squared Error = {mse}')\n",
        "print(f'Root Mean Squared Error = {rmse}')\n",
        "import numpy as np\n",
        "def mape(actual,pred):\n",
        "            return np.mean(np.abs((actual - pred) / actual)) * 100  \n",
        "print(f'Mean Absolute Percentage Error = {mape(np.array(test),np.array(test_predictions) )}')"
      ],
      "metadata": {
        "id": "3eW4RP_o9qZH",
        "outputId": "b4695db0-354f-430f-bf21-1d1632c31aa8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/base/model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  ConvergenceWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error = 5.518961517788971\n",
            "Mean Squared Error = 48.52526284540688\n",
            "Root Mean Squared Error = 6.966007669060297\n",
            "Mean Absolute Percentage Error = 36.591426512466505\n"
          ]
        }
      ]
    }
  ]
}