Книга_ARIMA_простой_пример.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN00IaQBEtx3/XFe1vKjm6F",
      "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/BrayNarvi/ARIMA/blob/main/%D0%9A%D0%BD%D0%B8%D0%B3%D0%B0_ARIMA_%D0%BF%D1%80%D0%BE%D1%81%D1%82%D0%BE%D0%B9_%D0%BF%D1%80%D0%B8%D0%BC%D0%B5%D1%80.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Устанавливаем библиотеку pmdarima в Colab"
      ],
      "metadata": {
        "id": "hyNwSaa3WYrS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LxoPh5WkWTad",
        "outputId": "a9461758-4936-41ba-c988-8df2b8f157eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pmdarima\n",
            "  Downloading pmdarima-2.0.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl (2.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.3.2)\n",
            "Requirement already satisfied: Cython!=0.29.18,!=0.29.31,>=0.29 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (3.0.8)\n",
            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.25.2)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.5.3)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.2.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.11.4)\n",
            "Requirement already satisfied: statsmodels>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (0.14.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (2.0.7)\n",
            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (67.7.2)\n",
            "Requirement already satisfied: packaging>=17.1 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (23.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2023.4)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22->pmdarima) (3.3.0)\n",
            "Requirement already satisfied: patsy>=0.5.4 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.13.2->pmdarima) (0.5.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.4->statsmodels>=0.13.2->pmdarima) (1.16.0)\n",
            "Installing collected packages: pmdarima\n",
            "Successfully installed pmdarima-2.0.4\n"
          ]
        }
      ],
      "source": [
        "!pip install pmdarima"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "aRpaV0c6Wkho"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Устанавливаем библиотеку statsmodels в Colab"
      ],
      "metadata": {
        "id": "bxmizpYVWmsb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install statsmodels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_eHHGTpCWo89",
        "outputId": "4157fed3-2382-4598-e1f2-7d62980c8d35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (0.14.1)\n",
            "Requirement already satisfied: numpy<2,>=1.18 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.25.2)\n",
            "Requirement already satisfied: scipy!=1.9.2,>=1.4 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.11.4)\n",
            "Requirement already satisfied: pandas!=2.1.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.5.3)\n",
            "Requirement already satisfied: patsy>=0.5.4 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (0.5.6)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (23.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2023.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.4->statsmodels) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Импортируем библиотеки"
      ],
      "metadata": {
        "id": "L20ZFSGzWr4n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from pmdarima import auto_arima\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf"
      ],
      "metadata": {
        "id": "MOiPkBRhWrDe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Теперь задаем данные для прогноза, берем данные из Яндекс.Вордстат"
      ],
      "metadata": {
        "id": "b_DVMiIhWwkW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Создаем DataFrame из предоставленных данных\n",
        "data = {\n",
        "    'Период': [\n",
        "        'февраль 2022', 'март 2022', 'апрель 2022', 'май 2022', 'июнь 2022',\n",
        "        'июль 2022', 'август 2022', 'сентябрь 2022', 'октябрь 2022', 'ноябрь 2022',\n",
        "        'декабрь 2022', 'январь 2023', 'февраль 2023', 'март 2023', 'апрель 2023',\n",
        "        'май 2023', 'июнь 2023', 'июль 2023', 'август 2023', 'сентябрь 2023',\n",
        "        'октябрь 2023', 'ноябрь 2023', 'декабрь 2023', 'январь 2024'\n",
        "    ],\n",
        "    'Число запросов': [\n",
        "        93075, 87171, 73510, 71260, 65263,\n",
        "        69453, 74285, 105472, 127480, 174382,\n",
        "        122724, 117438, 92277, 91291, 63133,\n",
        "        56414, 51961, 49480, 61993, 66098,\n",
        "        108766, 106576, 78193, 85181\n",
        "    ]\n",
        "}\n",
        "df = pd.DataFrame(data)"
      ],
      "metadata": {
        "id": "yH5bRuBVW5xT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ниже немного визуализации, чтобы посмотреть, что у нас за данные (можете не обращать внимания)"
      ],
      "metadata": {
        "id": "V0ISXhwyXR5K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Преобразуем период в datetime и устанавливаем его в качестве индекса\n",
        "df['Период'] = pd.to_datetime(df['Период'], format='%B %Y', errors='coerce')\n",
        "df.set_index('Период', inplace=True)\n",
        "\n",
        "# Визуализация ACF и PACF\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# ACF plot\n",
        "plt.subplot(121)\n",
        "plot_acf(df['Число запросов'], ax=plt.gca(), lags=12)\n",
        "\n",
        "# PACF plot\n",
        "plt.subplot(122)\n",
        "plot_pacf(df['Число запросов'], ax=plt.gca(), lags=12)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "iPUu9dbDXXoB",
        "outputId": "b6af8106-f3ec-43b6-d38e-19b8148c96f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Выше мы видим явную сезонность на интервале 12 месяцев. Поэтому задаем эту сезонность ниже."
      ],
      "metadata": {
        "id": "PaM2FUnzXj-1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Мы можем видеть сезонность с периодом в 12 месяцев (годовая сезонность)\n",
        "# Поэтому установим m=12\n",
        "m = 12"
      ],
      "metadata": {
        "id": "iR1ValY3Xqdu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Приступаем к обучению модели"
      ],
      "metadata": {
        "id": "00db-HRvXrg5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#импортируем библиотеку придупрещдений и далее в ответе фильтруем разные алерты, чтобы они нам не мешали. Можете отключить и посмотреть, что будет.\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Подбор параметров SARIMA модели\n",
        "auto_model = auto_arima(df['Число запросов'], seasonal=True, m=12, stepwise=True, trace=True, error_action='ignore')\n",
        "\n",
        "# Обучим модель SARIMA с найденными параметрами\n",
        "model = SARIMAX(df['Число запросов'], order=auto_model.order, seasonal_order=auto_model.seasonal_order).fit()\n",
        "\n",
        "# Сделаем прогноз на следующие 12 месяцев (год вперед)\n",
        "forecast = model.get_forecast(steps=12)\n",
        "pred = forecast.predicted_mean\n",
        "\n",
        "# Создаем новый DataFrame для прогноза\n",
        "forecast_df = pd.DataFrame({'Период': range(1, 13), 'Число запросов': pred.values})\n",
        "\n",
        "print(forecast_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgdPimhkXzHh",
        "outputId": "67609373-cdeb-4817-d324-961c6b43d6be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Performing stepwise search to minimize aic\n",
            " ARIMA(2,0,2)(1,0,1)[12] intercept   : AIC=555.404, Time=0.43 sec\n",
            " ARIMA(0,0,0)(0,0,0)[12] intercept   : AIC=564.377, Time=0.01 sec\n",
            " ARIMA(1,0,0)(1,0,0)[12] intercept   : AIC=549.751, Time=0.07 sec\n",
            " ARIMA(0,0,1)(0,0,1)[12] intercept   : AIC=559.142, Time=0.04 sec\n",
            " ARIMA(0,0,0)(0,0,0)[12]             : AIC=618.583, Time=0.02 sec\n",
            " ARIMA(1,0,0)(0,0,0)[12] intercept   : AIC=549.858, Time=0.02 sec\n",
            " ARIMA(1,0,0)(2,0,0)[12] intercept   : AIC=551.507, Time=0.15 sec\n",
            " ARIMA(1,0,0)(1,0,1)[12] intercept   : AIC=551.233, Time=0.11 sec\n",
            " ARIMA(1,0,0)(0,0,1)[12] intercept   : AIC=549.343, Time=0.17 sec\n",
            " ARIMA(1,0,0)(0,0,2)[12] intercept   : AIC=551.170, Time=0.32 sec\n",
            " ARIMA(1,0,0)(1,0,2)[12] intercept   : AIC=553.145, Time=0.18 sec\n",
            " ARIMA(0,0,0)(0,0,1)[12] intercept   : AIC=564.223, Time=0.06 sec\n",
            " ARIMA(2,0,0)(0,0,1)[12] intercept   : AIC=551.296, Time=0.10 sec\n",
            " ARIMA(1,0,1)(0,0,1)[12] intercept   : AIC=551.818, Time=0.07 sec\n",
            " ARIMA(2,0,1)(0,0,1)[12] intercept   : AIC=552.276, Time=0.14 sec\n",
            " ARIMA(1,0,0)(0,0,1)[12]             : AIC=553.134, Time=0.07 sec\n",
            "\n",
            "Best model:  ARIMA(1,0,0)(0,0,1)[12] intercept\n",
            "Total fit time: 2.080 seconds\n",
            "    Период  Число запросов\n",
            "0        1    72413.970575\n",
            "1        2    70671.415705\n",
            "2        3    58196.648472\n",
            "3        4    53520.279440\n",
            "4        5    50632.582368\n",
            "5        6    46996.572055\n",
            "6        7    50099.824885\n",
            "7        8    44997.381064\n",
            "8        9    58902.760627\n",
            "9       10    48470.132313\n",
            "10      11    43334.555229\n",
            "11      12    46196.102006\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "7NGU63C_ZOF1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Объединяем исходные данные и прогноз в один DataFrame\n",
        "df_with_forecast = df.append(forecast_df, ignore_index=True)\n",
        "\n",
        "# Выводим значения DataFrame\n",
        "print(df_with_forecast)\n",
        "\n",
        "# Устанавливаем стиль графика\n",
        "plt.style.use('seaborn-whitegrid')\n",
        "\n",
        "# Создаем график\n",
        "ax = df_with_forecast['Число запросов'].plot(style='-', color='green')\n",
        "\n",
        "# Добавляем легенду и подписываем оси\n",
        "plt.legend(loc='best')\n",
        "plt.xlabel('Число запросов')\n",
        "plt.ylabel('Номер строки')\n",
        "\n",
        "# Показываем график\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "g7isCi16ZOf_",
        "outputId": "d26c836c-551b-42bd-cff0-ea4d6e563efd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Число запросов  Период\n",
            "0     93075.000000     NaN\n",
            "1     87171.000000     NaN\n",
            "2     73510.000000     NaN\n",
            "3     71260.000000     NaN\n",
            "4     65263.000000     NaN\n",
            "5     69453.000000     NaN\n",
            "6     74285.000000     NaN\n",
            "7    105472.000000     NaN\n",
            "8    127480.000000     NaN\n",
            "9    174382.000000     NaN\n",
            "10   122724.000000     NaN\n",
            "11   117438.000000     NaN\n",
            "12    92277.000000     NaN\n",
            "13    91291.000000     NaN\n",
            "14    63133.000000     NaN\n",
            "15    56414.000000     NaN\n",
            "16    51961.000000     NaN\n",
            "17    49480.000000     NaN\n",
            "18    61993.000000     NaN\n",
            "19    66098.000000     NaN\n",
            "20   108766.000000     NaN\n",
            "21   106576.000000     NaN\n",
            "22    78193.000000     NaN\n",
            "23    85181.000000     NaN\n",
            "24    72413.970575     1.0\n",
            "25    70671.415705     2.0\n",
            "26    58196.648472     3.0\n",
            "27    53520.279440     4.0\n",
            "28    50632.582368     5.0\n",
            "29    46996.572055     6.0\n",
            "30    50099.824885     7.0\n",
            "31    44997.381064     8.0\n",
            "32    58902.760627     9.0\n",
            "33    48470.132313    10.0\n",
            "34    43334.555229    11.0\n",
            "35    46196.102006    12.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}