forecast_Favorita_Grocery_2_2_week.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/budennovsk/AuthorBooksComments/blob/master/forecast_Favorita_Grocery_2_2_week.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGMzxh-xsoXk",
        "outputId": "73ad60f9-720f-46fa-c66d-06d207d48eb2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.9.1->tsfresh) (2024.2.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22.0->tsfresh) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22.0->tsfresh) (3.3.0)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.13->tsfresh) (23.2)\n",
            "Requirement already satisfied: numba>=0.55.2 in /usr/local/lib/python3.10/dist-packages (from stumpy>=1.7.2->tsfresh) (0.58.1)\n",
            "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.55.2->stumpy>=1.7.2->tsfresh) (0.41.1)\n",
            "Installing collected packages: stumpy, tsfresh\n",
            "Successfully installed stumpy-1.12.0 tsfresh-0.20.2\n"
          ]
        }
      ],
      "source": [
        "!pip install catboost\n",
        "!pip install skforecast\n",
        "!pip install xgboost\n",
        "!pip install lightgbm\n",
        "!pip install nolds\n",
        "!pip install pmdarima\n",
        "!pip install feature-engine\n",
        "!pip install shap\n",
        "!pip install tsfresh"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "CwZYcLe7wtU0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "412142e1-bdfa-4fd0-9cd4-1ad3d5a843ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "AQHbF4lpTeEw"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "filtered_grouped_data = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/zvezda/MIO_1/Corporación Favorita Grocery Sales Forecasting/Favorita_Grocery_df.xlsx',\n",
        "                  index_col=[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "627Oq_138a_g"
      },
      "outputs": [],
      "source": [
        "def static_stacionar(data, SKU):\n",
        "    import matplotlib.pyplot as plt\n",
        "\n",
        "    random.seed(1)\n",
        "    np.random.seed(1)\n",
        "\n",
        "    end_train = '2016-06-01 23:59:59'\n",
        "    end_valid = '2017-01-01 23:59:59'\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # Добавление прогноза на график\n",
        "    plt.figure(figsize=(22, 6))\n",
        "    plt.plot(data.index[data.index<=end_train], data[:end_train], label='Тренеровачные данные')\n",
        "    plt.plot(data.index[(data.index <= end_valid) & (data.index >= end_train)], data[end_train:end_valid],label='Valid данные')\n",
        "    plt.plot(data.index[data.index >= end_valid], data[end_valid:],label='Test данные')\n",
        "    # Добавление точки на график\n",
        "\n",
        "    plt.xlabel('Дата')\n",
        "    plt.ylabel('Значение')\n",
        "    plt.title(f'График временного ряда с прогнозом forecaster SKU: {SKU}')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "    # diferenciator = TimeSeriesDifferentiator(order=1)\n",
        "    # data_diff = diferenciator.fit_transform(data)\n",
        "    # data_diff = pd.Series(data_diff, index=data.index).dropna()\n",
        "    # data_diff\n",
        "\n",
        "    print()\n",
        "    stl = STL(data, seasonal=13)\n",
        "    result = stl.fit()\n",
        "\n",
        "    fig, axes = plt.subplots(4, 1, sharex=False)\n",
        "    fig.suptitle(f' SKU: {SKU} STL Decomposition', fontsize=16)\n",
        "    fig.set_size_inches(22, 12)\n",
        "\n",
        "    # График исходных данных\n",
        "    axes[0].plot(result.observed)\n",
        "    axes[0].set_title('Observed')\n",
        "    axes[0].grid(True)\n",
        "\n",
        "    # График тренда\n",
        "    axes[1].plot(result.trend)\n",
        "    axes[1].set_title('Trend')\n",
        "    axes[1].grid(True)\n",
        "\n",
        "    # График сезонности\n",
        "    axes[2].plot(result.seasonal)\n",
        "    axes[2].set_title('Seasonality')\n",
        "    axes[2].grid(True)\n",
        "\n",
        "    # График остатка\n",
        "    axes[3].plot(result.resid)\n",
        "    axes[3].set_title('Residuals')\n",
        "    axes[3].grid(True)\n",
        "\n",
        "    # Добавляем пространство между графиками\n",
        "    plt.subplots_adjust(hspace=0.5)\n",
        "\n",
        "    # Показываем графики\n",
        "    plt.show()\n",
        "    # ===================================\n",
        "\n",
        "\n",
        "    print()\n",
        "\n",
        "    #=======================================\n",
        "\n",
        "    # acf_values_1 = acf(data)\n",
        "\n",
        "    # Создание подграфика с нужным размером\n",
        "    fig, ax = plt.subplots(figsize=(22, 6))\n",
        "\n",
        "    # Построение графика автокорреляционной функции с указанием подграфика для отображения\n",
        "    plot_acf(data, lags=47, ax=ax)\n",
        "\n",
        "    # Отображение графика\n",
        "    plt.show()\n",
        "    print()\n",
        "\n",
        "\n",
        "    # Создание подграфика с нужным размером\n",
        "    fig, ax = plt.subplots(figsize=(22, 6))\n",
        "\n",
        "    # Построение графика функции частичной автокорреляции с указанием подграфика для отображения\n",
        "    from statsmodels.graphics.tsaplots import plot_pacf\n",
        "    plot_pacf(data, lags=47, ax=ax)\n",
        "\n",
        "    # Отображение графика\n",
        "    plt.show()\n",
        "\n",
        "    print()\n",
        "    import nolds\n",
        "    from scipy.stats import entropy\n",
        "    from scipy.fft import fft\n",
        "    from statsmodels.stats.diagnostic import acorr_ljungbox\n",
        "    from statsmodels.tsa.stattools import adfuller\n",
        "\n",
        "\n",
        "    from statsmodels.tsa.stattools import kpss\n",
        "\n",
        "\n",
        "    coef_hurst = nolds.hurst_rs(data)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # Применение быстрого преобразования Фурье (FFT) к временному ряду\n",
        "    fft_result = fft(data.values.tolist())\n",
        "    # Вычисление амплитудного спектра\n",
        "    amplitude_spectrum = np.abs(fft_result)\n",
        "    # Нормализация амплитудного спектра\n",
        "    normalized_spectrum = amplitude_spectrum / np.sum(amplitude_spectrum)\n",
        "    # Вычисление спектральной энтропии\n",
        "    feat_spectral = entropy(normalized_spectrum,base=len(data))\n",
        "\n",
        "\n",
        "    box_pierce = acorr_ljungbox(data,boxpierce=True)\n",
        "    ljung_box = acorr_ljungbox(data)\n",
        "    #=============================\n",
        "    kpss_stat,p_value, lags,crit = kpss(data,regression = 'ct')\n",
        "\n",
        "\n",
        "\n",
        "    #=============================\n",
        "\n",
        "    adf,pvalue,usedlag,nobs,critical_values,icbest = adfuller(data)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # sm.graphics.tsa.plot_pacf(data, lags=52, method=\"ywm\")\n",
        "    # plt.show()\n",
        "\n",
        "    unitroot_kpss = kpss(data,regression = 'ct')\n",
        "\n",
        "    shift_level_index= data.rolling(window=10).mean().idxmax()\n",
        "    print()\n",
        "    print(f'SKU: {SKU} CТАТИСТИКА ОСНОВАНАЯ НА R')\n",
        "    print()\n",
        "    print('coef_hurst коэффициент Херста',coef_hurst)\n",
        "    if coef_hurst < 0.5:\n",
        "        print(\"Ряд имеет антипериодическую зависимость (значения в противоположных направлениях)\")\n",
        "    elif coef_hurst == 0.5:\n",
        "        print(\"Ряд не имеет зависимости\")\n",
        "    else:\n",
        "        print(\"Ряд имеет периодическую зависимость (значения в одном направлении)\")\n",
        "    print()\n",
        "    print('feat_spectral спектральную энтропию (Шеннона)',feat_spectral)\n",
        "    print()\n",
        "    print('box_pierce статистика Бокс-Пирса для проверки того, является ли временной ряд белым шумом,\\n',box_pierce)\n",
        "    print()\n",
        "    print('ljung_box cтатистика Юнга-Бокса для проверки того, является ли временной ряд белым шумом,\\n',ljung_box)\n",
        "    print()\n",
        "    print('shift_level_index указывает индекс, при котором происходит наибольший средний сдвиг',shift_level_index)\n",
        "    print()\n",
        "    print('____________________________________')\n",
        "    print()\n",
        "    print('unitroot_kpss статистика Квятковского-Филлипса-Шмидта-Шина (KPSS) для проверки, является ли ряд стационарным, \\n')\n",
        "    print()\n",
        "    # check p-value against significance level\n",
        "    if p_value < 0.05:\n",
        "        print('I По p-value, ряд НЕ стационарен метод KPSS')\n",
        "    else:\n",
        "        print('I По p-value, ряд стационарен метод KPSS')\n",
        "\n",
        "    # check KPSS statistic against critical value\n",
        "\n",
        "    if abs(kpss_stat) > abs(max(crit.values())):\n",
        "        print('II По значению KPSS статистики, ряд НЕ стационарен метод KPSS')\n",
        "    else:\n",
        "        print('II По значению KPSS статистики, ряд стационарен метод KPSS')\n",
        "    print()\n",
        "    print('kpss_stat', kpss_stat)\n",
        "    print('p_value', p_value)\n",
        "    print('lags', lags)\n",
        "    print('crit', crit)\n",
        "    # print('resstore', resstore)\n",
        "    print()\n",
        "    print('Дики — Фуллера проверки на стационарность , \\n')\n",
        "    print('kpss_stat', adf)\n",
        "    print('pvalue', pvalue)\n",
        "    print('usedlag', usedlag)\n",
        "    print('nobs', nobs)\n",
        "    print('critical_values', critical_values)\n",
        "\n",
        "    print('Results of Dickey-Fuller Test:')\n",
        "\n",
        "    if pvalue <= 0.05:\n",
        "        print('I По p-value, ряд стационарен метод ADF')\n",
        "    else:\n",
        "        print('I По p-value, ряд НЕ стационарен метод ADF')\n",
        "\n",
        "    # check ADF statistic against critical value\n",
        "    if abs(adf) > abs(max(critical_values.values())):\n",
        "        print('II По значению ADF статистики, ряд стационарен, метод ADF' )\n",
        "    else:\n",
        "        print('II По значению ADF статистики, ряд НЕ стационарен, метод ADF')\n",
        "    # print('resstore', resstore)\n",
        "\n",
        "    print()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "N-UQ7mZErdmB"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "# Функция для расчета MAPE\n",
        "def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "2GMcnrNNnUQa"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import scipy.stats as stats\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
        "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
        "from statsmodels.tsa.stattools import kpss\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "\n",
        "\n",
        "def static_resid(residuals,y_pred,SKU,model,name_regress):\n",
        "\n",
        "    # Работа с остатками после обученя модели\n",
        "\n",
        "    # График остатков\n",
        "    plt.subplots(figsize=(22, 6))\n",
        "    plt.scatter(y_pred, residuals)\n",
        "    plt.axhline(0, color='r', linestyle='--')\n",
        "    plt.xlabel('Предсказанные значения')\n",
        "    plt.ylabel('Остатки')\n",
        "    plt.title(f'SKU_{SKU}_{model}_{name_regress} График остатков')\n",
        "    plt.show()\n",
        "    print()\n",
        "\n",
        "    # Q-Q график\n",
        "    plt.subplots(figsize=(22, 6))\n",
        "    stats.probplot(residuals, dist='norm', plot=plt)\n",
        "    plt.title(f'SKU_{SKU}_{model}_{name_regress} Q-Q график остатков')\n",
        "    plt.show()\n",
        "    print()\n",
        "    # Диаграмма распределения остатков\n",
        "    plt.figure(figsize=(22, 6))\n",
        "    sns.histplot(residuals, kde=True)\n",
        "    plt.xlabel('Остатки')\n",
        "    plt.ylabel('Частота')\n",
        "    plt.title(f'SKU_{SKU}_{model}_{name_regress} Диаграмма распределения остатков')\n",
        "    plt.show()\n",
        "    print()\n",
        "\n",
        "    # Визуализация ACF остатков\n",
        "    fig, ax = plt.subplots(figsize=(22, 6))\n",
        "    # Построение графика автокорреляционной функции с указанием подграфика для отображения\n",
        "    plot_acf(residuals, lags=10, ax=ax)\n",
        "    # Отображение графика\n",
        "    plt.show()\n",
        "    print()\n",
        "\n",
        "    # Визуализация PACF остатков\n",
        "    fig, ax = plt.subplots(figsize=(22, 6))\n",
        "    plot_pacf(residuals, lags=10, ax=ax)\n",
        "    # Отображение графика\n",
        "    plt.show()\n",
        "    print()\n",
        "\n",
        "    print(f\"Статисткика по остаткам SKU_{SKU}_{model}_{name_regress}\")\n",
        "    # Применение теста Шапиро-Уилка к остаткам\n",
        "    shapiro_test = stats.shapiro(residuals)\n",
        "\n",
        "    # Проверка условия и вывод сообщения\n",
        "    if shapiro_test.pvalue >= 0.05:\n",
        "        print('Остатки временного ряда распределены согласно нормальному закону')\n",
        "    else:\n",
        "        print('Остатки временного ряда НЕ распределены согласно нормальному закону')\n",
        "    # Вывод результатов\n",
        "    print('Статистика теста Шапиро-Уилка :', shapiro_test.statistic)\n",
        "    print('p-value Шапиро-Уилка :', shapiro_test.pvalue)\n",
        "    print()\n",
        "    print('Тест Бокса-Пирса (BP) и Льюнга-Бокса (LB) на определение шума в остатках')\n",
        "    box_pierce = acorr_ljungbox(residuals,boxpierce=True)\n",
        "    box_pierce['result'] = box_pierce.apply(lambda row: 'белый шум' if row['lb_pvalue'] >= 0.05 and row['bp_pvalue'] >= 0.05 else 'не белый шум', axis=1)\n",
        "    print(box_pierce)\n",
        "    # Тест Дарбина-Уотсона\n",
        "    print()\n",
        "    dw_test = sm.stats.stattools.durbin_watson(residuals)\n",
        "    print('Тест Дарбина-Уотсона:', dw_test)\n",
        "    if dw_test < 1.0:\n",
        "        print(\"Положительная автокорреляция первого порядка в остатках.\")\n",
        "    elif dw_test > 3.0:\n",
        "        print(\"Отрицательная автокорреляция первого порядка в остатках.\")\n",
        "    else:\n",
        "        print(\"Отсутствие автокорреляции первого порядка в остатках.\")\n",
        "\n",
        "    print()\n",
        "\n",
        "    print('Тест на стационарность КПСС (Kwiatkowski-Phillips-Schmidt-Shin test)')\n",
        "    #KPSS\n",
        "    kpss_stat,p_value, lags,crit = kpss(residuals,regression = 'ct')\n",
        "    # check p-value against significance level\n",
        "    if p_value < 0.05:\n",
        "        print('I По p-value, ряд НЕ стационарен метод KPSS')\n",
        "    else:\n",
        "        print('I По p-value, ряд стационарен метод KPSS')\n",
        "\n",
        "    # check KPSS statistic against critical value\n",
        "\n",
        "    if abs(kpss_stat) > abs(max(crit.values())):\n",
        "        print('II По значению KPSS статистики, ряд НЕ стационарен метод KPSS')\n",
        "    else:\n",
        "        print('II По значению KPSS статистики, ряд стационарен метод KPSS')\n",
        "    print()\n",
        "    print('kpss_stat', kpss_stat)\n",
        "    print('p_value', p_value)\n",
        "    print('lags', lags)\n",
        "    print('crit', crit)\n",
        "\n",
        "\n",
        "\n",
        "    print()\n",
        "    print('Тест на стационарность Дики-Фуллера (Dickey-Fuller test')\n",
        "    adf,pvalue,usedlag,nobs,critical_values,icbest = adfuller(residuals)\n",
        "    print()\n",
        "    print('Дики — Фуллера проверки на стационарность , \\n')\n",
        "    print('kpss_stat', adf)\n",
        "    print('pvalue', pvalue)\n",
        "    print('usedlag', usedlag)\n",
        "    print('nobs', nobs)\n",
        "    print('critical_values', critical_values)\n",
        "\n",
        "    print('Results of Dickey-Fuller Test:')\n",
        "\n",
        "    if pvalue <= 0.05:\n",
        "        print('I По p-value, ряд стационарен метод ADF')\n",
        "    else:\n",
        "        print('I По p-value, ряд НЕ стационарен метод ADF')\n",
        "\n",
        "    # check ADF statistic against critical value\n",
        "    if abs(adf) > abs(max(critical_values.values())):\n",
        "        print('II По значению ADF статистики, ряд стационарен, метод ADF' )\n",
        "    else:\n",
        "        print('II По значению ADF статистики, ряд НЕ стационарен, метод ADF')\n",
        "    # print('resstore', resstore)\n",
        "\n",
        "    print()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "NCyTj0ZirkA-"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from catboost import CatBoostRegressor\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.api as sm\n",
        "\n",
        "from skforecast.ForecasterAutoreg import ForecasterAutoreg\n",
        "from skforecast.model_selection import backtesting_forecaster\n",
        "from skforecast.model_selection import grid_search_forecaster\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from xgboost import XGBRegressor\n",
        "from skforecast.ForecasterAutoreg import ForecasterAutoreg\n",
        "from skforecast.model_selection import backtesting_forecaster\n",
        "from skforecast.preprocessing import TimeSeriesDifferentiator\n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error, r2_score\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "from statsmodels.tsa.arima.model import ARIMA\n",
        "from statsmodels.tsa.seasonal import STL\n",
        "from math import sqrt\n",
        "from statsmodels.tsa.stattools import acf\n",
        "from statsmodels.graphics.tsaplots import plot_acf\n",
        "\n",
        "from skforecast.ForecasterBaseline import ForecasterEquivalentDate\n",
        "from skforecast.model_selection import backtesting_forecaster\n",
        "from skforecast.model_selection import grid_search_forecaster\n",
        "from skforecast.model_selection import bayesian_search_forecaster\n",
        "\n",
        "from lightgbm import LGBMRegressor\n",
        "\n",
        "from pmdarima.arima.utils import nsdiffs\n",
        "from pmdarima.arima.utils import ndiffs\n",
        "from feature_engine.creation import CyclicalFeatures\n",
        "from feature_engine.datetime import DatetimeFeatures\n",
        "import shap\n",
        "from statsmodels.graphics.tsaplots import plot_pacf\n",
        "from statsmodels.tsa.stattools import kpss\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_selection import RFE\n",
        "from pandas.tseries.offsets import DateOffset\n",
        "\n",
        "import random\n",
        "res_df = None\n",
        "\n",
        "\n",
        "def func_model(data, SKU):\n",
        "\n",
        "\n",
        "    random.seed(1)\n",
        "    np.random.seed(1)\n",
        "\n",
        "    end_train = '2016-06-01 23:59:59'\n",
        "    end_valid = '2017-01-01 23:59:59'\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # Добавление прогноза на график\n",
        "    plt.figure(figsize=(22, 6))\n",
        "    plt.plot(data.index[data.index<=end_train], data[:end_train], label='Тренеровачные данные')\n",
        "    plt.plot(data.index[(data.index <= end_valid) & (data.index >= end_train)], data[end_train:end_valid],label='Valid данные')\n",
        "    plt.plot(data.index[data.index >= end_valid], data[end_valid:],label='Test данные')\n",
        "    # Добавление точки на график\n",
        "    # plt.plot(random_date, data.loc[random_date], 'ro')  # 'ro' означает красные круглые маркеры\n",
        "    # plt.plot(random_date, data.loc[random_date]/1.5, 'yo')  # 'ro' означает красные круглые маркеры\n",
        "\n",
        "    plt.xlabel('Дата')\n",
        "    plt.ylabel('Значение')\n",
        "    plt.title(f'График временного ряда с прогнозом forecaster SKU: {SKU}')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "    # diferenciator = TimeSeriesDifferentiator(order=1)\n",
        "    # data_diff = diferenciator.fit_transform(data)\n",
        "    # data_diff = pd.Series(data_diff, index=data.index).dropna()\n",
        "    # data_diff\n",
        "\n",
        "\n",
        "    kpss_stat,p_value, lags,crit = kpss(data,regression = 'ct')\n",
        "    if abs(kpss_stat) > abs(max(crit.values())):\n",
        "        print('II По значению KPSS статистики, ряд НЕ стационарен метод KPSS')\n",
        "    else:\n",
        "        print('II По значению KPSS статистики, ряд стационарен метод KPSS')\n",
        "    print()\n",
        "    print('kpss_stat', kpss_stat)\n",
        "    print('p_value', p_value)\n",
        "    print('lags', lags)\n",
        "    print('crit', crit)\n",
        "\n",
        "    n_diffs = ndiffs(data, test='kpss')\n",
        "\n",
        "    print(\"ndiffs нужно ли делать дифференцацию:\", n_diffs)\n",
        "\n",
        "\n",
        "    # Определение количества сезонных разностей\n",
        "    n_seasonal_diffs = nsdiffs(data,m=4)\n",
        "\n",
        "    print(\"nsdiffs Сколько diff надо применить к данным:\", n_seasonal_diffs)\n",
        "\n",
        "\n",
        "\n",
        "    print()\n",
        "    stl = STL(data, seasonal=13)\n",
        "    result = stl.fit()\n",
        "\n",
        "    fig, axes = plt.subplots(4, 1, sharex=False)\n",
        "    fig.suptitle(f' SKU: {SKU} STL Decomposition', fontsize=16)\n",
        "    fig.set_size_inches(22, 12)\n",
        "\n",
        "    # График исходных данных\n",
        "    axes[0].plot(result.observed)\n",
        "    axes[0].set_title('Observed')\n",
        "    axes[0].grid(True)\n",
        "\n",
        "    # График тренда\n",
        "    axes[1].plot(result.trend)\n",
        "    axes[1].set_title('Trend')\n",
        "    axes[1].grid(True)\n",
        "\n",
        "    # График сезонности\n",
        "    axes[2].plot(result.seasonal)\n",
        "    axes[2].set_title('Seasonality')\n",
        "    axes[2].grid(True)\n",
        "\n",
        "    # График остатка\n",
        "    # axes[3].plot(result.resid)\n",
        "    # axes[3].set_title('Residuals')\n",
        "    # axes[3].grid(True)\n",
        "\n",
        "    axes[3].scatter(range(len(result.resid)), result.resid)\n",
        "    axes[3].set_title('Residuals')\n",
        "    axes[3].grid(True)\n",
        "    # Проведение прямой линии через y=0\n",
        "    axes[3].axhline(y=0, color='red', linestyle='--')\n",
        "\n",
        "    # Добавляем пространство между графиками\n",
        "    plt.subplots_adjust(hspace=0.5)\n",
        "\n",
        "    # Показываем графики\n",
        "    plt.show()\n",
        "    # ===================================\n",
        "\n",
        "\n",
        "    print()\n",
        "\n",
        "    # отстаки после обучени STL\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=[22, 12])\n",
        "    plot_acf(\n",
        "        x=result.resid,\n",
        "        lags=52,\n",
        "        ax=ax,\n",
        "        alpha=0.05\n",
        "    )\n",
        "    ax.set_title(\"Autocorrelation of residual after STL\")\n",
        "    ax.set_ylabel(\"Autcorrelation\")\n",
        "    ax.set_xlabel(\"Lag\")\n",
        "    # plt.tight_layout()\n",
        "    plt.show()\n",
        "    print()\n",
        "    #=======================================\n",
        "\n",
        "    # acf_values_1 = acf(data)\n",
        "\n",
        "    # Создание подграфика с нужным размером\n",
        "    fig, ax = plt.subplots(figsize=(22, 6))\n",
        "\n",
        "    # Построение графика автокорреляционной функции с указанием подграфика для отображения\n",
        "    plot_acf(data, lags=47,alpha=0.05,auto_ylims=True, ax=ax)\n",
        "\n",
        "    # Отображение графика\n",
        "    plt.show()\n",
        "    print()\n",
        "\n",
        "\n",
        "\n",
        "    # Создание подграфика с нужным размером\n",
        "    fig, ax = plt.subplots(figsize=(22, 6))\n",
        "\n",
        "    # Построение графика функции частичной автокорреляции с указанием подграфика для отображения\n",
        "\n",
        "    plot_pacf(data, lags=47,method='ywmle',alpha=0.05, auto_ylims=True,ax=ax)\n",
        "\n",
        "    # Отображение графика\n",
        "    plt.show()\n",
        "\n",
        "    #=======================================\n",
        "\n",
        "    # Data partition train-test\n",
        "    # ==============================================================================\n",
        "\n",
        "\n",
        "    print(\n",
        "        f\"Train dates : {data.index.min()} --- {data.loc[:end_train].index.max()}  \"\n",
        "        f\"(n={len(data.loc[:end_train])})\")\n",
        "    print(\n",
        "        f\"Valid dates  : {data.loc[end_train:].index.min()} --- {data.loc[:end_valid].index.max()}  \"\n",
        "        f\"(n={len(data.loc[end_train:end_valid])})\")\n",
        "\n",
        "    print(\n",
        "        f\"Test dates  : {data.loc[end_valid:].index.min()} --- {data.index.max()}  \"\n",
        "        f\"(n={len(data.loc[end_valid:])})\")\n",
        "\n",
        "\n",
        "    # Forecasting with differentiation\n",
        "    # ==============================================================================\n",
        "    steps = len(data.loc[end_train:])\n",
        "\n",
        "    lags_grid = [4,5, 8, [1, 2, 3, 27 ,52]]\n",
        "\n",
        "    # Regressor hyperparameters search space\n",
        "    def search_space_cb_52(trial):\n",
        "        search_space  = {\n",
        "            'depth' : trial.suggest_int('depth', 9, 12, step=1),\n",
        "            'learning_rate' : trial.suggest_float('learning_rate', 0.01, 0.5),\n",
        "            'iterations' : trial.suggest_int('iterations', 100, 300, step=100)\n",
        "        }\n",
        "        return search_space\n",
        "\n",
        "\n",
        "    calendar_features_52 = pd.DataFrame(index=data.index)\n",
        "    calendar_features_52['Values'] = data\n",
        "    calendar_features_52['month'] = calendar_features_52.index.month\n",
        "    calendar_features_52['week_of_year'] = calendar_features_52.index.isocalendar().week\n",
        "    calendar_features_52['year'] = calendar_features_52.index.year\n",
        "\n",
        "    calendar_features_52[\"month_sin\"] = np.sin(calendar_features_52[\"month\"] / calendar_features_52[\"month\"].max() * 2 * np.pi)\n",
        "    calendar_features_52[\"month_cos\"] = np.cos(calendar_features_52[\"month\"] / calendar_features_52[\"month\"].max() * 2 * np.pi)\n",
        "\n",
        "    calendar_features_52[\"week_of_year_sin\"] = np.sin(calendar_features_52[\"week_of_year\"] / calendar_features_52[\"week_of_year\"].max() * 2 * np.pi)\n",
        "    calendar_features_52[\"week_of_year_cos\"] = np.cos(calendar_features_52[\"week_of_year\"] / calendar_features_52[\"week_of_year\"].max() * 2 * np.pi)\n",
        "\n",
        "    transformer = DatetimeFeatures(\n",
        "                  variables           = \"index\",\n",
        "                  features_to_extract = \"all\" # It is also possible to select specific features\n",
        "              )\n",
        "    calendar_features_52 = transformer.fit_transform(calendar_features_52)\n",
        "    calendar_features_52.drop(['week','day_of_week','weekend','hour','minute','second'],inplace=True,axis=1)\n",
        "\n",
        "    calendar_features_52[\"quarter_sin\"] = np.sin(calendar_features_52[\"quarter\"] / calendar_features_52[\"quarter\"].max() * 2 * np.pi)\n",
        "    calendar_features_52[\"quarter_cos\"] = np.cos(calendar_features_52[\"quarter\"] / calendar_features_52[\"quarter\"].max() * 2 * np.pi)\n",
        "\n",
        "    calendar_features_52[\"semester_sin\"] = np.sin(calendar_features_52[\"semester\"] / calendar_features_52[\"semester\"].max() * 2 * np.pi)\n",
        "    calendar_features_52[\"semester_cos\"] = np.cos(calendar_features_52[\"semester\"] / calendar_features_52[\"semester\"].max() * 2 * np.pi)\n",
        "\n",
        "    calendar_features_52[\"day_of_month_sin\"] = np.sin(calendar_features_52[\"day_of_month\"] / calendar_features_52[\"day_of_month\"].max() * 2 * np.pi)\n",
        "    calendar_features_52[\"day_of_month_cos\"] = np.cos(calendar_features_52[\"day_of_month\"] / calendar_features_52[\"day_of_month\"].max() * 2 * np.pi)\n",
        "\n",
        "    calendar_features_52[\"day_of_year_sin\"] = np.sin(calendar_features_52[\"day_of_year\"] / calendar_features_52[\"day_of_year\"].max() * 2 * np.pi)\n",
        "    calendar_features_52[\"day_of_year_cos\"] = np.cos(calendar_features_52[\"day_of_year\"] / calendar_features_52[\"day_of_year\"].max() * 2 * np.pi)\n",
        "\n",
        "    # calendar_features_52['num_iter'] = range(0, len(data))\n",
        "\n",
        "    # cyclical = CyclicalFeatures(variables=None, drop_original=False)\n",
        "\n",
        "    # calendar_features_52 = cyclical.fit_transform(calendar_features_52[['month','week_of_year']])\n",
        "    global res_df\n",
        "    res_df = calendar_features_52\n",
        "    lags_num = 1\n",
        "\n",
        "\n",
        "\n",
        "    forecaster_cb_52_best_params_exog = ForecasterAutoreg(\n",
        "                    regressor = CatBoostRegressor(random_state=963,silent=True),\n",
        "                    lags      = lags_num\n",
        "                    # differentiation = 1\n",
        "                    # transformer_y    = StandardScaler()\n",
        "                    # transformer_y = MinMaxScaler()\n",
        "                    # transformer_exog = MinMaxScaler()\n",
        "\n",
        "                )\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # bayesian_search_forecaster(\n",
        "    #                                   forecaster         = forecaster_cb_52_best_params_exog,\n",
        "    #                                   y                  = calendar_features_52.loc[:end_valid,'Values'],\n",
        "    #                                   exog               = calendar_features_52.loc[:end_valid,calendar_features_52.columns.to_list()[1:]],\n",
        "    #                                   steps              = 52,\n",
        "    #                                   metric             = 'mean_absolute_error',\n",
        "    #                                   search_space       = search_space_cb_52,\n",
        "    #                                   lags_grid          = lags_grid,\n",
        "    #                                   initial_train_size = len(data[:end_train]),\n",
        "    #                                   refit              = False,\n",
        "    #                                   n_trials           = 20, # Increase for more exhaustive search\n",
        "    #                                   random_state       = 123,\n",
        "    #                                   return_best        = True,\n",
        "    #                                   n_jobs             = 'auto',\n",
        "    #                                   verbose            = False,\n",
        "    #                                   show_progress      = False\n",
        "    #                               )\n",
        "\n",
        "    # X_train, y_train = forecaster_cb_52_best_params_exog.create_train_X_y(\n",
        "    #                    y    = calendar_features_52.loc[:end_train,'Values'],\n",
        "    #                    exog = calendar_features_52.loc[:end_train,calendar_features_52.columns.to_list()[1:]])\n",
        "\n",
        "    X_train_1, y_train_1 = forecaster_cb_52_best_params_exog.create_train_X_y(\n",
        "                    y    = data['unit_sales'])\n",
        "\n",
        "\n",
        "\n",
        "    # forecaster_cb_52_best_params_exog.fit(y = calendar_features_52.loc[:end_train,'Values'],\n",
        "    #                                       exog =calendar_features_52.loc[:end_train,calendar_features_52.columns.to_list()[1:]]\n",
        "    #                                       )\n",
        "\n",
        "\n",
        "    # get_rfe(CatBoostRegressor(random_state=963,silent=True),\n",
        "    #         X_train_1,\n",
        "    #         y_train_1)\n",
        "\n",
        "    # get_import_feat(forecaster_cb_52_best_params_exog,\n",
        "    #                 calendar_features_52.loc[:end_train,calendar_features_52.columns.to_list()[1:]],\n",
        "    #                 calendar_features_52.loc[:end_train,'Values'])\n",
        "\n",
        "\n",
        "    data_windows_roll = get_windows(X_train_1)\n",
        "\n",
        "    data_polynom = get_polynom(calendar_features_52.join(data_windows_roll).join(X_train_1))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    data_tsfresh = tsfresh_func_1(X_train_1,y_train_1)\n",
        "\n",
        "    # print(data_tsfresh.columns.tolist())\n",
        "    # print(data_tsfresh)\n",
        "    y_new=pd.DataFrame()\n",
        "    y_new['Values']=y_train_1\n",
        "\n",
        "    data_tsfresh.index.freq = 'W-SUN'\n",
        "\n",
        "     # вывод сдвинутого DataFrame\n",
        "    shifted_df = calendar_features_52.index + pd.DateOffset(weeks=lags_num)\n",
        "\n",
        "\n",
        "    print('shape_ts',data_tsfresh.loc[:end_train,:].shape)\n",
        "    print('shape_calenda',calendar_features_52.loc[shifted_df[0]:end_train,calendar_features_52.columns.tolist()[1:]].shape)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    merged_df = pd.merge(data_tsfresh.loc[:,:],\n",
        "                         calendar_features_52.loc[shifted_df[0]:,calendar_features_52.columns.tolist()[1:]], left_index=True,right_index=True)\n",
        "    print(merged_df.shape)\n",
        "    merged_df_2 = pd.merge(merged_df, data_windows_roll,  left_index=True,right_index=True)\n",
        "    print(merged_df_2.shape)\n",
        "\n",
        "\n",
        "    final_merged_df = pd.merge(merged_df_2, data_polynom,  left_index=True,right_index=True)\n",
        "    print(final_merged_df.shape)\n",
        "\n",
        "    # final_merged_df_cal_wind = pd.merge(calendar_features_52.iloc[:,1:], data_windows_roll,  left_index=True,right_index=True)\n",
        "\n",
        "    # print('vvvvvvvvvvvvvvvv')\n",
        "    # print([i for i in final_merged_df_cal_wind if 'lag_1_window_3_mean' in i])\n",
        "    # print(final_merged_df_cal_wind.head().to_string())\n",
        "    # 22/0\n",
        "\n",
        "\n",
        "    best_n_splits = get_tscvoptimal(final_merged_df.loc[:end_train,:],y_new.loc[:end_train,'Values'])\n",
        "\n",
        "    selected_features_rfe = get_RFECV(final_merged_df.loc[:end_train,:],y_new.loc[:end_train,'Values'],best_n_splits)\n",
        "    final_merged_df['num_iter'] = range(0, len(final_merged_df))\n",
        "    selected_features_rfe = np.append(selected_features_rfe, 'num_iter')\n",
        "    print('fffffffffffffinal')\n",
        "    print('y')\n",
        "    print(y_new.loc[:,'Values'].shape)\n",
        "    print('x')\n",
        "    print(final_merged_df.loc[:,selected_features_rfe].shape)\n",
        "\n",
        "    data_all_norm, scarlet_mod = min_max_scaler(final_merged_df.loc[:,selected_features_rfe],\n",
        "                   y_new.loc[:,'Values'])\n",
        "\n",
        "    22/0\n",
        "\n",
        "    # bayesian_search_forecaster(\n",
        "    #                               forecaster         = forecaster_cb_52_best_params_exog,\n",
        "    #                               y                  = y_new.loc[:end_valid,'Values'],\n",
        "    #                               exog               = merged_df.loc[:end_valid,selected_features_rfe],\n",
        "    #                               steps              = 52,\n",
        "    #                               metric             = 'mean_absolute_error',\n",
        "    #                               search_space       = search_space_cb_52,\n",
        "    #                               lags_grid          = lags_grid,\n",
        "    #                               initial_train_size = len(data[:end_train]),\n",
        "    #                               refit              = False,\n",
        "    #                               n_trials           = 20, # Increase for more exhaustive search\n",
        "    #                               random_state       = 123,\n",
        "    #                               return_best        = True,\n",
        "    #                               n_jobs             = 'auto',\n",
        "    #                               verbose            = False,\n",
        "    #                               show_progress      = False\n",
        "    #                           )\n",
        "\n",
        "\n",
        "\n",
        "    forecaster_cb_52_best_params_exog.fit(y = y_new.loc[:end_train,'Values'],#y_new.loc[:end_train,'Values']\n",
        "                                          exog =final_merged_df.loc[:end_train,selected_features_rfe] #data_tsfresh.loc[:end_train,:]\n",
        "                                          )\n",
        "\n",
        "\n",
        "    print('fffff')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    X_train_2, y_train = forecaster_cb_52_best_params_exog.create_train_X_y(\n",
        "                       y    = y_new.loc[:end_train,'Values'],\n",
        "                       exog = final_merged_df.loc[:end_train,selected_features_rfe])\n",
        "    print(X_train_2.head().to_string())\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # print('y_fit',y_new.loc[:end_train,'Values'].head().to_string())\n",
        "    # print('exog_fit',data_tsfresh.loc[:end_train,:].head().to_string())\n",
        "    # print('exog_predict',data_tsfresh.loc[:end_train,:].head().to_string())\n",
        "    # print('exog_predict_merdge',merged_df.loc[end_train:,:].head().to_string())\n",
        "\n",
        "    # print('y_fit',y_new.loc[:end_train,'Values'].shape)\n",
        "    # print('exog_fit',data_tsfresh.loc[:end_train,:].shape)\n",
        "    # print('exog_predict',data_tsfresh.loc[:end_train,:].shape)\n",
        "\n",
        "    # print('last_wind',forecaster_cb_52_best_params_exog.last_window)\n",
        "    # print('window_size',forecaster_cb_52_best_params_exog.window_size)\n",
        "    imp = forecaster_cb_52_best_params_exog.get_feature_importances()\n",
        "\n",
        "    print('importance',imp.sort_values(by='importance', ascending=False)[:20])\n",
        "\n",
        "    # print('y_fit',y_new.loc[:end_train,'Values'].isna().sum())\n",
        "    # print('exog_fit',data_tsfresh.loc[:end_train,:].isna().sum())\n",
        "    # print('exog_predict',data_tsfresh.loc[:end_train,:].isna().sum())\n",
        "    predict_cb_52_best_params_exog = forecaster_cb_52_best_params_exog.predict(\n",
        "                                                            steps=steps,\n",
        "                                                            exog =final_merged_df.loc[end_train:,selected_features_rfe] #data_tsfresh.loc[end_train:,:]\n",
        "                                                                          )\n",
        "\n",
        "\n",
        "\n",
        "    # # Hyperparameters search tuning\n",
        "    # # ==============================================================================\n",
        "    # forecaster_cb_ht = ForecasterAutoreg(\n",
        "    #                 regressor = CatBoostRegressor(random_state=963,silent=True),\n",
        "    #                 lags      = 52,\n",
        "    #                 differentiation = 1\n",
        "    #             )\n",
        "\n",
        "\n",
        "    # bayesian_search_forecaster(\n",
        "    #                                   forecaster         = forecaster_cb_ht,\n",
        "    #                                   y                  = calendar_features_52.loc[:end_valid,'Values'],\n",
        "    #                                   exog               = calendar_features_52.loc[:end_valid,calendar_features_52.columns.to_list()[1:]],\n",
        "    #                                   steps              = 10,\n",
        "    #                                   metric             = 'mean_absolute_error',\n",
        "    #                                   search_space       = search_space_cb_52,\n",
        "    #                                   lags_grid          = lags_grid,\n",
        "    #                                   initial_train_size = len(data[:end_train]),\n",
        "    #                                   refit              = False,\n",
        "    #                                   n_trials           = 20, # Increase for more exhaustive search\n",
        "    #                                   random_state       = 123,\n",
        "    #                                   return_best        = True,\n",
        "    #                                   n_jobs             = 'auto',\n",
        "    #                                   verbose            = False,\n",
        "    #                                   show_progress      = False\n",
        "    #                               )\n",
        "\n",
        "\n",
        "    # metric_cb_back_ht, predictions_cb_back_ht = backtesting_forecaster(\n",
        "    #                           forecaster         = forecaster_cb_ht,\n",
        "    #                           y                  = calendar_features_52['Values'],\n",
        "    #                           exog               = calendar_features_52.iloc[:,1:],\n",
        "    #                           steps              = 10,\n",
        "    #                           metric             = 'mean_absolute_error',\n",
        "    #                           initial_train_size = len(data.loc[:end_train]),\n",
        "    #                           refit              = False,\n",
        "    #                           n_jobs             = 'auto',\n",
        "    #                           verbose            = False,\n",
        "    #                           show_progress      = False\n",
        "    #                       )\n",
        "\n",
        "    # # # backtesting hyper tuning errors___________\n",
        "    # # # Расчет MAE\n",
        "\n",
        "    # error_cb_back_ht = mean_absolute_error(data.loc[end_train:], predictions_cb_back_ht)\n",
        "\n",
        "\n",
        "    # # Расчет RMSE\n",
        "    # error_rmse_cb_back_ht = mean_squared_error(data.loc[end_train:], predictions_cb_back_ht, squared=False)\n",
        "\n",
        "    # r2_cb_back_ht = r2_score(data.loc[end_train:], predictions_cb_back_ht)\n",
        "\n",
        "    # mape_cb_back_ht = mean_absolute_percentage_error(data.loc[end_train:], predictions_cb_back_ht)\n",
        "\n",
        "\n",
        "    # Plot\n",
        "    fig, ax = plt.subplots(figsize=(22, 6), sharex=True, sharey=True)\n",
        "    data.loc[:end_train,'unit_sales'].plot(ax=ax, label='train',color='#1f77b4')\n",
        "    data.loc[end_train:,'unit_sales'].plot(ax=ax, label='test',color='black')\n",
        "    predict_cb_52_best_params_exog.plot(ax=ax, label='Catboost',color='green')\n",
        "\n",
        "    ax.set_title(f'SKU: {SKU} *_1_3 model Forecasting with differentiation Huper tuning EXOG 52 недели рекурентно без добавление новых данных')\n",
        "    ax.set_xlabel('')\n",
        "    ax.grid(True)\n",
        "    ax.legend()\n",
        "    plt.show()\n",
        "    print()\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(22, 6), sharex=True, sharey=True)\n",
        "    data.loc[end_train:,'unit_sales'].plot(ax=ax, label='test', color='black')\n",
        "    predict_cb_52_best_params_exog.plot(ax=ax, label='Catboost', color='green')\n",
        "\n",
        "    ax.set_title(f'SKU: {SKU} *_1_3 model max SIZE Forecasting with differentiation Huper tuning EXOG 52 недели рекурентно без добавление новых данных')\n",
        "    ax.set_xlabel('')\n",
        "    ax.grid(True)\n",
        "    ax.legend()\n",
        "    plt.show()\n",
        "    print()\n",
        "\n",
        "\n",
        "    dict_regress = {'Catboost':predict_cb_52_best_params_exog}\n",
        "\n",
        "    for regress in dict_regress:\n",
        "        residuals = data.loc[end_train:]['unit_sales'] - dict_regress[regress]\n",
        "        static_resid(residuals,dict_regress[regress],SKU, '*_1_3 model',regress)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # #================back_ht\n",
        "    # fig, ax = plt.subplots(figsize=(22, 6), sharex=True, sharey=True)\n",
        "    # data.loc[:end_train].plot(ax=ax, label='train',color='#1f77b4')\n",
        "    # data.loc[end_train:].plot(ax=ax, label='test',color='black')\n",
        "    # predictions_cb_back_ht['pred'].plot(ax=ax, label='Catboost',color='green')\n",
        "\n",
        "    # ax.set_title(f'SKU: {SKU} *_2_3 Forecasting with differentiation with Backtesting with Hyper Tuning + EXOG по 10 недель добавляет данные из test в обучение')\n",
        "    # ax.set_xlabel('')\n",
        "    # ax.grid(True)\n",
        "    # ax.legend()\n",
        "    # plt.show()\n",
        "    # print()\n",
        "\n",
        "    # fig, ax = plt.subplots(figsize=(22, 6), sharex=True, sharey=True)\n",
        "    # data.loc[end_train:].plot(ax=ax, label='test',color='black')\n",
        "    # predictions_cb_back_ht['pred'].plot(ax=ax, label='Catboost',color='green')\n",
        "\n",
        "    # ax.set_title(f'SKU: {SKU} *_2_3 max SIZE Forecasting with differentiation with Backtesting with Hyper Tuning + EXOG по 10 недель добавляет данные из test в обучение')\n",
        "    # ax.set_xlabel('')\n",
        "    # ax.grid(True)\n",
        "    # ax.legend()\n",
        "    # plt.show()\n",
        "    # print()\n",
        "\n",
        "\n",
        "    # dict_regress_2 = {'Catboost':predictions_cb_back_ht}\n",
        "\n",
        "    # for regress_2 in dict_regress_2:\n",
        "    #     residuals_2 = data.loc[end_train:]['unit_sales'] - dict_regress[regress_2]\n",
        "    #     static_resid(residuals_2, dict_regress[regress_2],SKU, '*_2_3 model',regress_2)\n",
        "\n",
        "\n",
        "\n",
        "    # #==============52 недели без обучения + EXOG дучшая модель ================\n",
        "\n",
        "\n",
        "    # Расчет MAE\n",
        "\n",
        "    error_cb_ht_52_exog = mean_absolute_error(data.loc[end_train:], predict_cb_52_best_params_exog)\n",
        "\n",
        "\n",
        "    # Расчет RMSE\n",
        "    error_rmse_cb_ht_52_exog = mean_squared_error(data.loc[end_train:], predict_cb_52_best_params_exog, squared=False)\n",
        "\n",
        "\n",
        "    # Расчет R2\n",
        "    r2_cb_back_ht_52_exog = r2_score(data.loc[end_train:], predict_cb_52_best_params_exog)\n",
        "\n",
        "\n",
        "    mape_cb_back_ht_52_exog = mean_absolute_percentage_error(data.loc[end_train:], predict_cb_52_best_params_exog)\n",
        "\n",
        "\n",
        "    print()\n",
        "    print(f'SKU: {SKU} *_1_3 model Ошибки при прогнозе Catboost прогноз + huper tuning + EXOG на 52 недели без добавление новых данных без переучивании,XGB')\n",
        "    # Вывод результатов\n",
        "    print(f\"Error (MAE) - средняя абсолютная ошибка Catboost: {error_cb_ht_52_exog:.2f}\")\n",
        "\n",
        "    print(f\"Error (RMSE) - Среднеквадратичная ошибка Catboost: {error_rmse_cb_ht_52_exog:.2f}\")\n",
        "\n",
        "    print(f\"R2 - Коэффициент детерминации Catboost: {r2_cb_back_ht_52_exog:.2f}\")\n",
        "\n",
        "    print(f\"MAPE - Средняя абсолютная процентная ошибка Catboost: {mape_cb_back_ht_52_exog:.2f} %\")\n",
        "\n",
        "    print()\n",
        "    print()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # #Backtesting hyper tuning ______________errors\n",
        "\n",
        "    # print(f'SKU: {SKU} *_2_3 model Ошибки при прогнозе backtesting hyper tuning + EXOG Catboost,XGB, LGB по 10 недель добавляет данные из test в обучение')\n",
        "    # # Вывод результатов\n",
        "    # print(f\"Error backtesting hyper tuning (MAE) - средняя абсолютная ошибка Catboost: {error_cb_back_ht:.2f}\")\n",
        "\n",
        "    # print(f\"Error backtesting hyper tuning (RMSE) - Среднеквадратичная ошибка Catboost: {error_rmse_cb_back_ht:.2f}\")\n",
        "\n",
        "    # print(f\"R2 - backtesting hyper tuning Коэффициент детерминации Catboost: {r2_cb_back_ht:.2f}\")\n",
        "\n",
        "    # print(f\"MAPE - backtesting hyper tuning Средняя абсолютная процентная ошибка Catboost: {mape_cb_back_ht:.2f} %\")\n",
        "\n",
        "    # print()\n",
        "\n",
        "    # print()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "47NWSB994HTz"
      },
      "outputs": [],
      "source": [
        "def get_rfe(model, X,y):\n",
        "\n",
        "    rfe = RFE(estimator=model, n_features_to_select=6)\n",
        "    rfe.fit(X,y)\n",
        "    print(X.shape[1])\n",
        "\n",
        "    for i, col in zip(range(X.shape[1]),X.columns):\n",
        "        print(f'{col} selected={rfe.support_[i]} rank = {rfe.ranking_[i]}')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a custom metric for the MAD\n",
        "def mad(x):\n",
        "\n",
        "    return np.median(np.abs(x - np.median(x)))"
      ],
      "metadata": {
        "id": "5iTLx-BfuKXg"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_import_feat(model,x,y):\n",
        "        # Predictors importances\n",
        "        # ==============================================================================\n",
        "    imp = model.get_feature_importances()\n",
        "\n",
        "    print(imp.sort_values(by='importance', ascending=False)[:20])\n",
        "\n",
        "\n",
        "        # Create SHAP explainer\n",
        "    X_train, y_train = model.create_train_X_y(\n",
        "                       y    = y,\n",
        "                       exog = x\n",
        "                   )\n",
        "    print(X_train)\n",
        "    # ==============================================================================\n",
        "    explainer = shap.TreeExplainer(model.regressor)\n",
        "    shap_values = explainer.shap_values(X_train)\n",
        "    shap.summary_plot(shap_values, X_train, plot_type=\"bar\")\n",
        "    shap.summary_plot(shap_values, X_train)"
      ],
      "metadata": {
        "id": "rXzM_d4Lynsw"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "fEwkJeWOEFc1"
      },
      "outputs": [],
      "source": [
        "from catboost import CatBoostRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import matplotlib.pylab as plt\n",
        "\n",
        "from tsfresh import extract_features, extract_relevant_features, select_features\n",
        "from tsfresh.utilities.dataframe_functions import impute\n",
        "from tsfresh.feature_extraction import ComprehensiveFCParameters\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "\n",
        "X_filtered_g_1=None\n",
        "def tsfresh_func_1(X,y):\n",
        "\n",
        "\n",
        "\n",
        "    extraction_settings = ComprehensiveFCParameters()\n",
        "    # lag_last = X.columns.tolist()[-1]\n",
        "    X['id'] = range(len(X))\n",
        "    X['time']=X.index\n",
        "    # X['y']=y\n",
        "    X.reset_index(inplace=True)\n",
        "    X.drop(['date'],inplace=True,axis=1)\n",
        "\n",
        "\n",
        "\n",
        "    # X_filtered = extract_relevant_features(X, X['y'], column_id='id', column_sort='time',\n",
        "\n",
        "    #                                         default_fc_parameters=extraction_settings)\n",
        "\n",
        "    X_filtered = extract_features(X, column_id='id', column_sort='time',\n",
        "                                            impute_function=impute)\n",
        "\n",
        "    X_filtered=X_filtered.set_index(X['time'])\n",
        "    del_lag = [i for i in X_filtered.columns.tolist() if 'sum_values' in i]\n",
        "    # X_filtered.drop([i for i in X_filtered.columns.tolist() if 'y' in i[0]],axis=1,inplace=True)\n",
        "    X_filtered.drop(del_lag,axis=1,inplace=True)\n",
        "\n",
        "    global X_filtered_g_1\n",
        "    X_filtered_g_1 = X_filtered\n",
        "\n",
        "    return X_filtered\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from feature_engine.timeseries.forecasting import WindowFeatures\n",
        "def get_windows(X):\n",
        "    df = X.copy()\n",
        "    df_lag = X.copy()\n",
        "\n",
        "    lag_del = df.columns.tolist()\n",
        "\n",
        "\n",
        "    # Create a custom metric for the MAD\n",
        "\n",
        "    # Create window features using the transformer.\n",
        "    transformer = WindowFeatures(\n",
        "        variables=df.columns.tolist(),\n",
        "        functions=[\"mean\", \"std\",'min','max',\"kurt\", \"skew\"],\n",
        "        window=[3, 4,4*2,4*3,4*4] # Day, week, year.\n",
        "        # freq=\"1H\",\n",
        "    )\n",
        "\n",
        "    df = transformer.fit_transform(df)\n",
        "\n",
        "    mad_lag=pd.DataFrame()\n",
        "\n",
        "    # Процесс итерации и применения скользящих окон\n",
        "    for iter_lag in df_lag.columns.tolist():\n",
        "        for window_size in [3, 4, 8, 12, 16]:\n",
        "            mad_lag[f'mad_{window_size}_{iter_lag}'] = df_lag[iter_lag].rolling(window=window_size).apply(mad)\n",
        "\n",
        "    mad_lag = mad_lag.fillna(0)\n",
        "\n",
        "\n",
        "    df = df.fillna(0)\n",
        "    df.drop(lag_del,axis=1,inplace=True)\n",
        "    result = mad_lag.join(df)\n",
        "\n",
        "    return result\n"
      ],
      "metadata": {
        "id": "iS2uQhaA90NF"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import RFECV\n",
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def get_RFECV(X_train,y_train, best_n_splits):\n",
        "    regressor = LGBMRegressor(\n",
        "                n_estimators = 100,\n",
        "                max_depth    = 5,\n",
        "                random_state = 15926,\n",
        "                verbose      = -1\n",
        "            )\n",
        "    # regressor =  CatBoostRegressor(random_state=963,silent=True)\n",
        "\n",
        "    min_features_to_select = 1\n",
        "    tscv = TimeSeriesSplit(n_splits=int(best_n_splits))  #int(best_n_splits)\n",
        "\n",
        "    selector = RFECV(\n",
        "        estimator              = regressor,\n",
        "        step                   = 1,\n",
        "        cv                     = tscv,\n",
        "        min_features_to_select = min_features_to_select,\n",
        "        scoring=\"neg_mean_squared_error\",\n",
        "        n_jobs                 = -1\n",
        "    )\n",
        "\n",
        "    selector.fit(X_train, y_train)\n",
        "\n",
        "    selected_features_rfe = selector.get_feature_names_out()\n",
        "\n",
        "    print(\"Recursive feature elimination\")\n",
        "    print(\"-----------------------------\")\n",
        "    print(f\"Total number of features available: {X_train.shape[1]}\")\n",
        "    print(f\"Total number of records available: {X_train.shape[0]}\")\n",
        "    print(f\"Total number of records used for feature selection: {X_train.shape[0]}\")\n",
        "    print(f\"Number of features selected: {len(selected_features_rfe)}\")\n",
        "    print(f\"Selected features : \\n {selected_features_rfe}\")\n",
        "\n",
        "    print('vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv')\n",
        "\n",
        "    # Получение оценок ошибок на кросс-валидации из cv_results_\n",
        "    neg_mean_squared_errors = selector.cv_results_['mean_test_score'][:20]\n",
        "    print(neg_mean_squared_errors)\n",
        "    # Предположим, что neg_mean_squared_errors уже получен из предыдущего вызова функции\n",
        "    min_neg_mean_squared_error = max(neg_mean_squared_errors)\n",
        "\n",
        "    # Вывести минимальное значение среднеквадратичной ошибки\n",
        "    print(\"Минимальная среднеквадратичная ошибка:\", min_neg_mean_squared_error)\n",
        "\n",
        "    n_scores = len(selector.cv_results_[\"mean_test_score\"][:20])\n",
        "    plt.figure()\n",
        "    plt.xlabel(\"Number of features selected\")\n",
        "    plt.ylabel(\"Mean test RMSE\")\n",
        "    plt.errorbar(\n",
        "        range(min_features_to_select, n_scores + min_features_to_select),\n",
        "        selector.cv_results_[\"mean_test_score\"][:20],\n",
        "        yerr=selector.cv_results_[\"std_test_score\"][:20],\n",
        "    )\n",
        "    plt.title(\"Recursive Feature Elimination \\nwith correlated features\")\n",
        "    plt.show()\n",
        "    return selected_features_rfe\n"
      ],
      "metadata": {
        "id": "xq6YkIrTiXWi"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import TimeSeriesSplit, cross_val_score\n",
        "from catboost import CatBoostRegressor\n",
        "import numpy as np\n",
        "\n",
        "def get_tscvoptimal(X,y):\n",
        "\n",
        "\n",
        "    # Создание модели\n",
        "    model = CatBoostRegressor(verbose=False)\n",
        "\n",
        "    # Значения n_splits для проверки\n",
        "    n_splits_values = [2, 3, 4, 5] #[2, 3, 4, 5]\n",
        "\n",
        "    # Словарь для сохранения среднего значения оценок\n",
        "    mean_scores = {}\n",
        "    mean_scores_1 = []\n",
        "\n",
        "    # Проход по всем значениям n_splits\n",
        "    for n_splits in n_splits_values:\n",
        "        tscv = TimeSeriesSplit(n_splits=n_splits)\n",
        "        scores = cross_val_score(model, X, y, cv=tscv, scoring='neg_mean_squared_error') #'neg_mean_squared_error', 'r2'\n",
        "        mean_scores[n_splits] = np.mean(scores)\n",
        "        mean_scores_1.append(np.mean(scores))\n",
        "\n",
        "    # Нахождение оптимального значения n_splits\n",
        "    best_n_splits = max(mean_scores, key=mean_scores.get)\n",
        "    print(f'Оптимальное значение n_splits: {best_n_splits}')\n",
        "    print(mean_scores_1)\n",
        "    # Построение графика\n",
        "    plt.plot(n_splits_values, mean_scores_1, marker='o')\n",
        "    plt.xlabel('n_splits')\n",
        "    plt.ylabel('Neg Mean Squared Error')\n",
        "    plt.title('n_splits vs Neg Mean Squared Error')\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "    return best_n_splits"
      ],
      "metadata": {
        "id": "q-j4gHWgHYZ0"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "# !jupyter notebook --NotebookApp.iopub_data_rate_limit=10000000\n",
        "\n",
        "def get_polynom(df):\n",
        "    df = df.dropna()\n",
        "    df.drop(['Values'],inplace=True,axis=1)\n",
        "    print(df.head().to_string())\n",
        "    print(df.shape)\n",
        "\n",
        "    transformer_poly = PolynomialFeatures(\n",
        "                       degree           = 2,\n",
        "                       interaction_only = True,\n",
        "                       include_bias     = False\n",
        "                   ).set_output(transform=\"pandas\")\n",
        "    tr = 10\n",
        "\n",
        "    poly_features = transformer_poly.fit_transform(df.iloc[:,:tr])\n",
        "    poly_features = poly_features.drop(columns=df.columns.tolist()[:tr])\n",
        "    poly_features.columns = [f\"poly_{col}\" for col in poly_features.columns]\n",
        "    poly_features.columns = poly_features.columns.str.replace(\" \", \"__\")\n",
        "\n",
        "    print(poly_features.shape)\n",
        "    # print(poly_features.head(4).to_string())\n",
        "    return poly_features\n",
        "\n"
      ],
      "metadata": {
        "id": "jDY19gzbPKDT"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "def min_max_scaler(X,y):\n",
        "    print('xxxxxxxxx',X.head().to_string())\n",
        "    print('yyyyyyyyyyy',y.head().to_string())\n",
        "\n",
        "    X['y']= y\n",
        "    df_all = X.copy()\n",
        "    print(df_all.shape)\n",
        "    print('alll',df_all.head().to_string())\n",
        "\n",
        "    scaler = MinMaxScaler()\n",
        "    norm_data = pd.DataFrame(scaler.fit_transform(df_all),columns = df_all.columns)\n",
        "    print('norm',norm_data.head().to_string())\n",
        "    return norm_data, scaler\n"
      ],
      "metadata": {
        "id": "xXOQS3_W049c"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EtEGyqyTxPpN",
        "outputId": "42f38c14-fa22-499a-a7ea-c28a7d0e05f7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The test statistic is outside of the range of p-values available in the\n",
            "look-up table. The actual p-value is smaller than the p-value returned.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "II По значению KPSS статистики, ряд НЕ стационарен метод KPSS\n",
            "\n",
            "kpss_stat 0.368302824539519\n",
            "p_value 0.01\n",
            "lags 6\n",
            "crit {'10%': 0.119, '5%': 0.146, '2.5%': 0.176, '1%': 0.216}\n",
            "ndiffs нужно ли делать дифференцацию: 1\n",
            "nsdiffs Сколько diff надо применить к данным: 0\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2200x1200 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2200x1200 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2200x600 with 1 Axes>"
            ],
            "image/png": 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1bjl5t7bS/63vbq8JVJNqaJcqXUNHH3/cXvX56RNtj4ZUTnLEXoMq/juphEq/D6qlho7muwNUt45sl6r186nIayBjab9XN/I1a9eQkr169crw4cMzY8aMlmXNzc2ZMWNGRo8e3eY+o0ePbrV9kvzsZz9b7/YAAFvKoLreOXHkbjn78LfkxJG7+cIMVMzYoQPaDNuS1V/cDxs6sCPLAai4ner65IxDh6RUWt1j4PU/zzh0iP+30eF8dwASn0+0T7ujzIkTJ2bChAkZMWJERo4cmcsuuyzLli3LKaeckiQ56aSTsssuu2Tq1KlJknPOOSdjxozJV7/61Rx55JG56aab8sgjj+Saa67ZvM8EAIA3tKDp5ZZ/3/rovIzbqz471fWpYEVUivdCZa354n71A3PWGa7KF3eguxqz58AMre+nVxEAVcXnExur3YHbCSeckMWLF2fy5MlZsGBBhg0blunTp6e+vj5J8txzz6Wm5rWOcwceeGBuvPHGXHTRRbnwwgvzlre8JXfccUf23XffzfcsAAB4QzNnL8o1D85puT/9iQX56RMLcsahQzJmT71puhPvhergizvAutb0KgKAauLziY2xSYN1nnXWWTnrrLPaXDdz5sx1lh133HE57rjjNuVQAABsBvMbl+eaB+ek/Lox7NZM+nz1A3MytL6fk/zdhPdCdfHFHQAAoGto1xxuAAB0TjNnL05pPetKSe6bvagjy6GCvBcAAABg8xO4AdAtrD1X0fzG5RWsBjre4qUrUl7PuvL/rad78F4AAACAzU/gBkCXN3P2olx4++9b7k9/YkHOv/V3uf+PenHQfQzoW7vBXk0D+tZ2ZDlUkPcCAABA5+Ni8uoncAOgS1vfXEXl8uq5ihY0vrz+naELGTt0wAZ7NR02dGBHlkMFeS8AAAB0Li4m7xwEbgB0aeYqgtV2quuTMw4dklIpqSml1c8zDh2SQXW9K10iHcR7AQAAoPNwMXnn0bPSBQDAlmSuInjNmD0HZmh9v9w3e1EWL12RAX1rc9jQgR0esKw9DMa4veqzU12fbldDJVXLewEAAIANW3MxeVvnt9ZcTH7iyN06uCraInADoEtbM1fR+v5TYq4iuptBdb0r+h/xmbMX5ZoH57Tcn/7Egvz0iQU549AhGbNnxwxlWA01VINKvxcAgLZ19wuDAGjNxeSdhyElAejSzFUE1aMahsGohhoAqE5rhxzzG5dXsBq6K3P0ALC2NReTt8XF5NVF4AZAl2auIqge1TCnYjXUwGpObAPVRMhBNXBhEABtcTF552FISQC6PHMVQXWohmEwqqEGDOsJVJf1hRzJ6pBjaH0//2+kQ5ijB4C2rLmY/OoH5rR8Tqz56WLy6iJwA6BbMFcRVF41zKlYDTV0d05sA9VGyEG1cGEQAOvjYvLOwZCSAAB0iGoYBqMaaujuDOsJVBshB9XCHD0AbMiai8nPPvwtOXHkbsK2KiRwAwCgQ1TDnIrVUEN358Q2UG2EHFQLFwYBQOdmSEkAADpMNQyDUQ01dGeG9QSqzdihA3Ln48+3uU7IQUcyRw8AdG4CNwAAOlQ1zKlY6RoWNL3c8u9bH52XcXvVZ6e6PhWrpyM5sQ1UGyEH1cSFQQDQeQncAACgA82cvSjXPDin5f70Jxbkp08syBmHDsmYPbt+2OTENlCNhBxUk0pfGJR074uDgOqkXaIzELgBAEAHmd+4PNc8OCfl142n2Px//776gTkZWt+vW5zcdWIbqEbVEHJANejuFwcB1Ue7RGdRU+kCgK5r7StP5jcur2A1AFB5M2cvTmk960pJ7pu9qCPLqag1J7bPPvwtOXHkbsI2AKgC67s4qFxefXHQgsaX178zwBagXaIzEbgBW8TM2Yty4e2/b7k//YkFOf/W3+X+P3afE4kAsLbFS1ekvJ515f9bDwBQKS4OAqqNdonOROAGbHauPAGAtg3oW7vBL4sD+tZ2ZDkAAK24OAioNtolOhOBG7DZufIEANo2duiADX5ZPGyo+QcAgMpxcRBQbbRLdCYCN2Czc+UJALRtp7o+OePQISmVkppSWv0849Ah5jEDACrKxUFAtdEu0Zn0rHQBQNez5sqTtj4MXXkCQHc3Zs+BGVrfL/fNXpTFS1dkQN/aHDZ0oLANAKi4NRcHXf3AnJbv9Wt+ujgIqATtEp2JwA3Y7MYOHZA7H3++zXWuPAGAZFBd75w4crdKlwEAsA4XBwHVRrtEZyFwAzY7V54AAABA5+XiIKDaaJfoDARuwBbhyhMAAAAAALoLgRuwxbjyBAAAAACA7qCm0gUAAAAAUHkLml5u+fetj87L/MblFawGAKBzEbgBAAAAdHMzZy/Khbf/vuX+9CcW5Pxbf5f7/7ioglUBAHQeAjcAAACAbmx+4/Jc8+CclMuvLWsuJ+VycvUDc7Kg8eX17wwAQBKBGwAAAEBVqNSQjjNnL05pPetKSe6brZcbAMAbEbgBAAAAVFglh3RcvHRFyutZV/6/9QAAbJjADQAAAKCCKj2k44C+tRvs4Tagb+0WPT4AQFcgcAMAAACooEoP6Th26IAN9nA7bOjALXp8AICuQOAGAAAAUEGVHtJxp7o+OePQISmVkppSWv0849AhGVTXe4seHwCgK+hZ6QKobqPftGOlSwAAAIAu7YGnFufhuX/NqvK6sVtNqZRhDdtt8e/no9+0Yz70jt1y8yPz8ue/Lc+u2/fJCSMaMrj/Nlv0uAAAXYXADQAAAKCCjh/RkKvvf6bNdeVyOSeMaOiQOgb33yafec9bO+RYAABdjSElAQAAACpoj/7b5Msf3C81paRHTanVzy9/cD+9zAAAOgE93AAAAAAq7LgRDXnH4B0M6QgA0EkJ3AAAAACqgCEdAQA6L0NKAgAAAAAAQAECNwAAAAAAAChA4AYAAAAAAAAFCNwAAAAAAACggJ6VLmBjlMvlJElTU1OFKwEAAAAAAKC7WJNNrcmq1qdTBG4vvvhikqShoaHClQAAAAAAANDdvPjii6mrq1vv+lL5jSK5KtDc3Jznn38+2267bUqlUqXL6RSamprS0NCQefPmpV+/fpUuB6AqaBsBWtMuArSmXQRoTbsIsLpn24svvpidd945NTXrn6mtU/Rwq6mpya677lrpMjqlfv36+TAEWIu2EaA17SJAa9pFgNa0i0B3t6GebWusP4oDAAAAAAAA3pDADQAAAAAAAAoQuHVRtbW1mTJlSmpraytdCkDV0DYCtKZdBGhNuwjQmnYRYOOVyuVyudJFAAAAAAAAQGelhxsAAAAAAAAUIHADAAAAAACAAgRuAAAAAAAAUIDADQAAAAAAAAoQuHVRV155ZQYPHpzevXtn1KhRefjhhytdEkCHeOCBB3LUUUdl5513TqlUyh133NFqfblczuTJk7PTTjulT58+GTduXJ566qnKFAvQAaZOnZp3vOMd2XbbbTNw4MAcc8wxmT17dqttXn755Zx55pnZcccd07dv33zwgx/MwoULK1QxwJZ11VVXZb/99ku/fv3Sr1+/jB49Oj/96U9b1msTge7uS1/6UkqlUs4999yWZdpGgDcmcOuCbr755kycODFTpkzJY489lv333z/jx4/PokWLKl0awBa3bNmy7L///rnyyivbXP9v//Zv+drXvpZp06bl17/+dbbZZpuMHz8+L7/8cgdXCtAx7r///px55pn5f//v/+VnP/tZXnnllbz73e/OsmXLWrY577zzcuedd+bWW2/N/fffn+effz5///d/X8GqAbacXXfdNV/60pfy6KOP5pFHHsnhhx+ev/u7v8t///d/J9EmAt3bb37zm1x99dXZb7/9Wi3XNgK8sVK5XC5Xugg2r1GjRuUd73hHrrjiiiRJc3NzGhoa8slPfjIXXHBBhasD6DilUim33357jjnmmCSre7ftvPPOOf/88/OpT30qSdLY2Jj6+vpcd911+dCHPlTBagE6xuLFizNw4MDcf//9OfTQQ9PY2JgBAwbkxhtvzLHHHpskefLJJ7PXXnvloYceyjvf+c4KVwyw5e2www7593//9xx77LHaRKDbWrp0aQ444IB84xvfyBe+8IUMGzYsl112mf8vAmwkPdy6mJUrV+bRRx/NuHHjWpbV1NRk3LhxeeihhypYGUDlzZ07NwsWLGjVRtbV1WXUqFHaSKDbaGxsTLL65HKSPProo3nllVdatY1vfetbs9tuu2kbgS5v1apVuemmm7Js2bKMHj1amwh0a2eeeWaOPPLIVm1g4v+LABurZ6ULYPNasmRJVq1alfr6+lbL6+vr8+STT1aoKoDqsGDBgiRps41csw6gK2tubs65556bgw46KPvuu2+S1W1jr169st1227XaVtsIdGW///3vM3r06Lz88svp27dvbr/99uy9996ZNWuWNhHolm666aY89thj+c1vfrPOOv9fBNg4AjcAAOgmzjzzzDzxxBP5xS9+UelSACpq6NChmTVrVhobG3PbbbdlwoQJuf/++ytdFkBFzJs3L+ecc05+9rOfpXfv3pUuB6DTMqRkF9O/f//06NEjCxcubLV84cKFGTRoUIWqAqgOa9pBbSTQHZ111ln5yU9+kvvuuy+77rpry/JBgwZl5cqVeeGFF1ptr20EurJevXrlzW9+c4YPH56pU6dm//33z+WXX65NBLqlRx99NIsWLcoBBxyQnj17pmfPnrn//vvzta99LT179kx9fb22EWAjCNy6mF69emX48OGZMWNGy7Lm5ubMmDEjo0ePrmBlAJW3xx57ZNCgQa3ayKampvz617/WRgJdVrlczllnnZXbb7899957b/bYY49W64cPH56tttqqVds4e/bsPPfcc9pGoNtobm7OihUrtIlAt/Sud70rv//97zNr1qyW24gRI/KRj3yk5d/aRoA3ZkjJLmjixImZMGFCRowYkZEjR+ayyy7LsmXLcsopp1S6NIAtbunSpXn66adb7s+dOzezZs3KDjvskN122y3nnntuvvCFL+Qtb3lL9thjj3z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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dates : 2013-01-06 00:00:00 --- 2016-05-29 00:00:00  (n=178)\n",
            "Valid dates  : 2016-06-05 00:00:00 --- 2017-01-01 00:00:00  (n=31)\n",
            "Test dates  : 2017-01-08 00:00:00 --- 2017-08-13 00:00:00  (n=32)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "WARNING:tsfresh.feature_extraction.settings:Dependency not available for matrix_profile, this feature will be disabled!\n",
            "WARNING:tsfresh.feature_extraction.settings:Dependency not available for matrix_profile, this feature will be disabled!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            month  week_of_year  year  month_sin  month_cos  week_of_year_sin  week_of_year_cos  quarter  semester  day_of_month  day_of_year  month_start  month_end  quarter_start  quarter_end  year_start  year_end  leap_year  days_in_month  quarter_sin   quarter_cos  semester_sin  semester_cos  day_of_month_sin  day_of_month_cos  day_of_year_sin  day_of_year_cos  mad_3_lag_1  mad_4_lag_1  mad_8_lag_1  mad_12_lag_1  mad_16_lag_1  lag_1_window_3_mean  lag_1_window_3_std  lag_1_window_3_min  lag_1_window_3_max  lag_1_window_3_kurt  lag_1_window_3_skew  lag_1_window_4_mean  lag_1_window_4_std  lag_1_window_4_min  lag_1_window_4_max  lag_1_window_4_kurt  lag_1_window_4_skew  lag_1_window_8_mean  lag_1_window_8_std  lag_1_window_8_min  lag_1_window_8_max  lag_1_window_8_kurt  lag_1_window_8_skew  lag_1_window_12_mean  lag_1_window_12_std  lag_1_window_12_min  lag_1_window_12_max  lag_1_window_12_kurt  lag_1_window_12_skew  lag_1_window_16_mean  lag_1_window_16_std  lag_1_window_16_min  lag_1_window_16_max  lag_1_window_16_kurt  lag_1_window_16_skew  lag_1\n",
            "date                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       \n",
            "2013-01-13      1             2  2013   0.500000   0.866025          0.234886          0.972023        1         1            13           13            0          0              0            0           0         0          0             31          1.0  6.123234e-17  1.224647e-16          -1.0          0.485302         -0.874347         0.223124         0.974790          0.0          0.0          0.0           0.0           0.0             0.000000            0.000000                 0.0                 0.0                  0.0             0.000000                  0.0            0.000000                 0.0                 0.0             0.000000             0.000000                  0.0                 0.0                 0.0                 0.0                  0.0                  0.0                   0.0                  0.0                  0.0                  0.0                   0.0                   0.0                   0.0                  0.0                  0.0                  0.0                   0.0                   0.0  126.0\n",
            "2013-01-20      1             3  2013   0.500000   0.866025          0.348202           0.93742        1         1            20           20            0          0              0            0           0         0          0             31          1.0  6.123234e-17  1.224647e-16          -1.0         -0.790776         -0.612106         0.339308         0.940675          0.0          0.0          0.0           0.0           0.0             0.000000            0.000000                 0.0                 0.0                  0.0             0.000000                  0.0            0.000000                 0.0                 0.0             0.000000             0.000000                  0.0                 0.0                 0.0                 0.0                  0.0                  0.0                   0.0                  0.0                  0.0                  0.0                   0.0                   0.0                   0.0                  0.0                  0.0                  0.0                   0.0                   0.0  149.0\n",
            "2013-01-27      1             4  2013   0.500000   0.866025          0.456629          0.889657        1         1            27           27            0          0              0            0           0         0          0             31          1.0  6.123234e-17  1.224647e-16          -1.0         -0.724793          0.688967         0.450517         0.892768          5.0          0.0          0.0           0.0           0.0             0.000000            0.000000                 0.0                 0.0                  0.0             0.000000                  0.0            0.000000                 0.0                 0.0             0.000000             0.000000                  0.0                 0.0                 0.0                 0.0                  0.0                  0.0                   0.0                  0.0                  0.0                  0.0                   0.0                   0.0                   0.0                  0.0                  0.0                  0.0                   0.0                   0.0  144.0\n",
            "2013-02-03      2             5  2013   0.866025   0.500000          0.558647          0.829406        1         1             3           34            0          0              0            0           0         0          0             28          1.0  6.123234e-17  1.224647e-16          -1.0          0.571268          0.820763         0.555120         0.831770          1.0          3.0          0.0           0.0           0.0           139.666667           12.096832               126.0               149.0                  0.0            -1.405138                  0.0            0.000000                 0.0                 0.0             0.000000             0.000000                  0.0                 0.0                 0.0                 0.0                  0.0                  0.0                   0.0                  0.0                  0.0                  0.0                   0.0                   0.0                   0.0                  0.0                  0.0                  0.0                   0.0                   0.0  143.0\n",
            "2013-02-10      2             6  2013   0.866025   0.500000          0.652822          0.757511        1         1            10           41            0          0              0            0           0         0          0             28          1.0  6.123234e-17  1.224647e-16          -1.0          0.897805         -0.440394         0.651584         0.758576          1.0          2.5          0.0           0.0           0.0           145.333333            3.214550               143.0               149.0                  0.0             1.545393                140.5           10.016653               126.0               149.0             2.928378            -1.576113                  0.0                 0.0                 0.0                 0.0                  0.0                  0.0                   0.0                  0.0                  0.0                  0.0                   0.0                   0.0                   0.0                  0.0                  0.0                  0.0                   0.0                   0.0  149.0\n",
            "(240, 63)\n",
            "(240, 45)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Feature Extraction: 100%|██████████| 240/240 [00:08<00:00, 28.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape_ts (177, 782)\n",
            "shape_calenda (177, 27)\n",
            "(240, 809)\n",
            "(240, 844)\n",
            "(240, 889)\n",
            "Оптимальное значение n_splits: 2\n",
            "[-393.2974569703341, -417.08671352172877, -411.70167476754807, -413.2350101595647]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recursive feature elimination\n",
            "-----------------------------\n",
            "Total number of features available: 889\n",
            "Total number of records available: 177\n",
            "Total number of records used for feature selection: 177\n",
            "Number of features selected: 20\n",
            "Selected features : \n",
            " ['lag_1__abs_energy' 'day_of_month_cos' 'mad_4_lag_1' 'lag_1_window_3_std'\n",
            " 'lag_1_window_3_skew' 'lag_1_window_4_mean' 'lag_1_window_4_max'\n",
            " 'lag_1_window_4_kurt' 'lag_1_window_4_skew' 'lag_1_window_8_kurt'\n",
            " 'lag_1_window_12_mean' 'lag_1_window_12_kurt' 'lag_1_window_12_skew'\n",
            " 'lag_1_window_16_std' 'lag_1_window_16_kurt' 'lag_1_window_16_skew'\n",
            " 'poly_month_sin__week_of_year_sin' 'poly_month_cos__week_of_year_sin'\n",
            " 'poly_quarter__day_of_month' 'poly_semester__day_of_month']\n",
            "vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv\n",
            "[-399.82104918 -398.78024001 -396.09295391 -418.59391609 -395.73059636\n",
            " -413.32997085 -413.86479039 -436.81251595 -416.45525911 -401.07665773\n",
            " -388.86212687 -391.33331749 -391.73441892 -383.62385965 -401.94974506\n",
            " -391.4734357  -391.17123461 -396.7390705  -377.70248388 -376.82245039]\n",
            "Минимальная среднеквадратичная ошибка: -376.822450394135\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fffffffffffffinal\n",
            "y\n",
            "(240,)\n",
            "x\n",
            "(240, 21)\n",
            "xxxxxxxxx             lag_1__abs_energy  day_of_month_cos  mad_4_lag_1  lag_1_window_3_std  lag_1_window_3_skew  lag_1_window_4_mean  lag_1_window_4_max  lag_1_window_4_kurt  lag_1_window_4_skew  lag_1_window_8_kurt  lag_1_window_12_mean  lag_1_window_12_kurt  lag_1_window_12_skew  lag_1_window_16_std  lag_1_window_16_kurt  lag_1_window_16_skew  poly_month_sin__week_of_year_sin  poly_month_cos__week_of_year_sin  poly_quarter__day_of_month  poly_semester__day_of_month  num_iter\n",
            "time                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n",
            "2013-01-13            15876.0         -0.874347          0.0            0.000000             0.000000                  0.0                 0.0             0.000000             0.000000                  0.0                   0.0                   0.0                   0.0                  0.0                   0.0                   0.0                          0.117443                          0.203417                        13.0                         13.0         0\n",
            "2013-01-20            22201.0         -0.612106          0.0            0.000000             0.000000                  0.0                 0.0             0.000000             0.000000                  0.0                   0.0                   0.0                   0.0                  0.0                   0.0                   0.0                          0.174101                          0.301551                        20.0                         20.0         1\n",
            "2013-01-27            20736.0          0.688967          0.0            0.000000             0.000000                  0.0                 0.0             0.000000             0.000000                  0.0                   0.0                   0.0                   0.0                  0.0                   0.0                   0.0                          0.228315                          0.395453                        27.0                         27.0         2\n",
            "2013-02-03            20449.0          0.820763          3.0           12.096832            -1.405138                  0.0                 0.0             0.000000             0.000000                  0.0                   0.0                   0.0                   0.0                  0.0                   0.0                   0.0                          0.483802                          0.279323                         3.0                          3.0         3\n",
            "2013-02-10            22201.0         -0.440394          2.5            3.214550             1.545393                140.5               149.0             2.928378            -1.576113                  0.0                   0.0                   0.0                   0.0                  0.0                   0.0                   0.0                          0.565361                          0.326411                        10.0                         10.0         4\n",
            "yyyyyyyyyyy date\n",
            "2013-01-13    149.0\n",
            "2013-01-20    144.0\n",
            "2013-01-27    143.0\n",
            "2013-02-03    149.0\n",
            "2013-02-10    145.0\n",
            "Freq: W-SUN\n",
            "(240, 22)\n",
            "alll             lag_1__abs_energy  day_of_month_cos  mad_4_lag_1  lag_1_window_3_std  lag_1_window_3_skew  lag_1_window_4_mean  lag_1_window_4_max  lag_1_window_4_kurt  lag_1_window_4_skew  lag_1_window_8_kurt  lag_1_window_12_mean  lag_1_window_12_kurt  lag_1_window_12_skew  lag_1_window_16_std  lag_1_window_16_kurt  lag_1_window_16_skew  poly_month_sin__week_of_year_sin  poly_month_cos__week_of_year_sin  poly_quarter__day_of_month  poly_semester__day_of_month  num_iter      y\n",
            "time                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          \n",
            "2013-01-13            15876.0         -0.874347          0.0            0.000000             0.000000                  0.0                 0.0             0.000000             0.000000                  0.0                   0.0                   0.0                   0.0                  0.0                   0.0                   0.0                          0.117443                          0.203417                        13.0                         13.0         0  149.0\n",
            "2013-01-20            22201.0         -0.612106          0.0            0.000000             0.000000                  0.0                 0.0             0.000000             0.000000                  0.0                   0.0                   0.0                   0.0                  0.0                   0.0                   0.0                          0.174101                          0.301551                        20.0                         20.0         1  144.0\n",
            "2013-01-27            20736.0          0.688967          0.0            0.000000             0.000000                  0.0                 0.0             0.000000             0.000000                  0.0                   0.0                   0.0                   0.0                  0.0                   0.0                   0.0                          0.228315                          0.395453                        27.0                         27.0         2  143.0\n",
            "2013-02-03            20449.0          0.820763          3.0           12.096832            -1.405138                  0.0                 0.0             0.000000             0.000000                  0.0                   0.0                   0.0                   0.0                  0.0                   0.0                   0.0                          0.483802                          0.279323                         3.0                          3.0         3  149.0\n",
            "2013-02-10            22201.0         -0.440394          2.5            3.214550             1.545393                140.5               149.0             2.928378            -1.576113                  0.0                   0.0                   0.0                   0.0                  0.0                   0.0                   0.0                          0.565361                          0.326411                        10.0                         10.0         4  145.0\n",
            "   lag_1__abs_energy  day_of_month_cos  mad_4_lag_1  lag_1_window_3_std  lag_1_window_3_skew  lag_1_window_4_mean  lag_1_window_4_max  lag_1_window_4_kurt  lag_1_window_4_skew  lag_1_window_8_kurt  lag_1_window_12_mean  lag_1_window_12_kurt  lag_1_window_12_skew  lag_1_window_16_std  lag_1_window_16_kurt  lag_1_window_16_skew  poly_month_sin__week_of_year_sin  poly_month_cos__week_of_year_sin  poly_quarter__day_of_month  poly_semester__day_of_month  num_iter         y\n",
            "0           0.164004          0.060416     0.000000            0.000000             0.500000             0.000000            0.000000             0.594902             0.506795              0.30998                   0.0              0.339512              0.529261                  0.0              0.262951              0.506945                          0.166789                          0.817887                    0.100840                     0.196721  0.000000  0.419355\n",
            "1           0.324407          0.191874     0.000000            0.000000             0.500000             0.000000            0.000000             0.594902             0.506795              0.30998                   0.0              0.339512              0.529261                  0.0              0.262951              0.506945                          0.220306                          0.899351                    0.159664                     0.311475  0.004184  0.379032\n",
            "2           0.287254          0.844083     0.000000            0.000000             0.500000             0.000000            0.000000             0.594902             0.506795              0.30998                   0.0              0.339512              0.529261                  0.0              0.262951              0.506945                          0.271514                          0.977300                    0.218487                     0.426230  0.008368  0.370968\n",
            "3           0.279976          0.910151     0.088235            0.206570             0.094371             0.000000            0.000000             0.594902             0.506795              0.30998                   0.0              0.339512              0.529261                  0.0              0.262951              0.506945                          0.512837                          0.880899                    0.016807                     0.032787  0.012552  0.419355\n",
            "4           0.324407          0.277951     0.073529            0.054893             0.946116             0.778393            0.674208             0.894050             0.105249              0.30998                   0.0              0.339512              0.529261                  0.0              0.262951              0.506945                          0.589873                          0.919987                    0.075630                     0.147541  0.016736  0.387097\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ZeroDivisionError",
          "evalue": "division by zero",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-80-af7a28b7470f>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mfunc_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweekly_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msku\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0;31m# static_stacionar(weekly_df[:-1], sku)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-69-291dd4ae0d8f>\u001b[0m in \u001b[0;36mfunc_model\u001b[0;34m(data, SKU)\u001b[0m\n\u001b[1;32m    375\u001b[0m     min_max_scaler(final_merged_df.loc[:,selected_features_rfe],\n\u001b[1;32m    376\u001b[0m                    y_new.loc[:,'Values'])\n\u001b[0;32m--> 377\u001b[0;31m     \u001b[0;36m22\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m     \u001b[0;31m# bayesian_search_forecaster(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
          ]
        }
      ],
      "source": [
        "for sku in range(1,2):\n",
        "    even_df = filtered_grouped_data[filtered_grouped_data['store_nbr']==sku][['date','unit_sales']]\n",
        "    even_df['date'] = pd.to_datetime(even_df['date'])\n",
        "    even_df = even_df.set_index('date')\n",
        "    even_df = even_df.asfreq('D')\n",
        "    weekly_df = even_df.resample('W').sum()\n",
        "\n",
        "\n",
        "    func_model(weekly_df[:-1],sku)\n",
        "    # static_stacionar(weekly_df[:-1], sku)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "Orx-vJfnnmsR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "5abd2b9c-c786-422c-fa5e-1a034d4251ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Values  month  week_of_year  year  month_sin     month_cos  \\\n",
              "date                                                                     \n",
              "2013-01-06   126.0      1             1  2013   0.500000  8.660254e-01   \n",
              "2013-01-13   149.0      1             2  2013   0.500000  8.660254e-01   \n",
              "2013-01-20   144.0      1             3  2013   0.500000  8.660254e-01   \n",
              "2013-01-27   143.0      1             4  2013   0.500000  8.660254e-01   \n",
              "2013-02-03   149.0      2             5  2013   0.866025  5.000000e-01   \n",
              "2013-02-10   145.0      2             6  2013   0.866025  5.000000e-01   \n",
              "2013-02-17   121.0      2             7  2013   0.866025  5.000000e-01   \n",
              "2013-02-24   166.0      2             8  2013   0.866025  5.000000e-01   \n",
              "2013-03-03   160.0      3             9  2013   1.000000  6.123234e-17   \n",
              "2013-03-10   147.0      3            10  2013   1.000000  6.123234e-17   \n",
              "\n",
              "            week_of_year_sin  week_of_year_cos  quarter  semester  ...  \\\n",
              "date                                                               ...   \n",
              "2013-01-06          0.118273          0.992981        1         1  ...   \n",
              "2013-01-13          0.234886          0.972023        1         1  ...   \n",
              "2013-01-20          0.348202           0.93742        1         1  ...   \n",
              "2013-01-27          0.456629          0.889657        1         1  ...   \n",
              "2013-02-03          0.558647          0.829406        1         1  ...   \n",
              "2013-02-10          0.652822          0.757511        1         1  ...   \n",
              "2013-02-17          0.737833          0.674983        1         1  ...   \n",
              "2013-02-24          0.812487          0.582979        1         1  ...   \n",
              "2013-03-03          0.875735          0.482792        1         1  ...   \n",
              "2013-03-10           0.92669          0.375828        1         1  ...   \n",
              "\n",
              "            leap_year  days_in_month  quarter_sin   quarter_cos  semester_sin  \\\n",
              "date                                                                            \n",
              "2013-01-06          0             31          1.0  6.123234e-17  1.224647e-16   \n",
              "2013-01-13          0             31          1.0  6.123234e-17  1.224647e-16   \n",
              "2013-01-20          0             31          1.0  6.123234e-17  1.224647e-16   \n",
              "2013-01-27          0             31          1.0  6.123234e-17  1.224647e-16   \n",
              "2013-02-03          0             28          1.0  6.123234e-17  1.224647e-16   \n",
              "2013-02-10          0             28          1.0  6.123234e-17  1.224647e-16   \n",
              "2013-02-17          0             28          1.0  6.123234e-17  1.224647e-16   \n",
              "2013-02-24          0             28          1.0  6.123234e-17  1.224647e-16   \n",
              "2013-03-03          0             31          1.0  6.123234e-17  1.224647e-16   \n",
              "2013-03-10          0             31          1.0  6.123234e-17  1.224647e-16   \n",
              "\n",
              "            semester_cos  day_of_month_sin  day_of_month_cos  day_of_year_sin  \\\n",
              "date                                                                            \n",
              "2013-01-06          -1.0          0.937752          0.347305         0.103668   \n",
              "2013-01-13          -1.0          0.485302         -0.874347         0.223124   \n",
              "2013-01-20          -1.0         -0.790776         -0.612106         0.339308   \n",
              "2013-01-27          -1.0         -0.724793          0.688967         0.450517   \n",
              "2013-02-03          -1.0          0.571268          0.820763         0.555120   \n",
              "2013-02-10          -1.0          0.897805         -0.440394         0.651584   \n",
              "2013-02-17          -1.0         -0.299363         -0.954139         0.738494   \n",
              "2013-02-24          -1.0         -0.988468          0.151428         0.814576   \n",
              "2013-03-03          -1.0          0.571268          0.820763         0.878714   \n",
              "2013-03-10          -1.0          0.897805         -0.440394         0.929968   \n",
              "\n",
              "            day_of_year_cos  \n",
              "date                         \n",
              "2013-01-06         0.994612  \n",
              "2013-01-13         0.974790  \n",
              "2013-01-20         0.940675  \n",
              "2013-01-27         0.892768  \n",
              "2013-02-03         0.831770  \n",
              "2013-02-10         0.758576  \n",
              "2013-02-17         0.674260  \n",
              "2013-02-24         0.580057  \n",
              "2013-03-03         0.477349  \n",
              "2013-03-10         0.367642  \n",
              "\n",
              "[10 rows x 28 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bc1bd26a-63f3-4f24-b182-00a675e7f28c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Values</th>\n",
              "      <th>month</th>\n",
              "      <th>week_of_year</th>\n",
              "      <th>year</th>\n",
              "      <th>month_sin</th>\n",
              "      <th>month_cos</th>\n",
              "      <th>week_of_year_sin</th>\n",
              "      <th>week_of_year_cos</th>\n",
              "      <th>quarter</th>\n",
              "      <th>semester</th>\n",
              "      <th>...</th>\n",
              "      <th>leap_year</th>\n",
              "      <th>days_in_month</th>\n",
              "      <th>quarter_sin</th>\n",
              "      <th>quarter_cos</th>\n",
              "      <th>semester_sin</th>\n",
              "      <th>semester_cos</th>\n",
              "      <th>day_of_month_sin</th>\n",
              "      <th>day_of_month_cos</th>\n",
              "      <th>day_of_year_sin</th>\n",
              "      <th>day_of_year_cos</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",
              "      <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>2013-01-06</th>\n",
              "      <td>126.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2013</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>8.660254e-01</td>\n",
              "      <td>0.118273</td>\n",
              "      <td>0.992981</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.123234e-17</td>\n",
              "      <td>1.224647e-16</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.937752</td>\n",
              "      <td>0.347305</td>\n",
              "      <td>0.103668</td>\n",
              "      <td>0.994612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-13</th>\n",
              "      <td>149.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2013</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>8.660254e-01</td>\n",
              "      <td>0.234886</td>\n",
              "      <td>0.972023</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.123234e-17</td>\n",
              "      <td>1.224647e-16</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.485302</td>\n",
              "      <td>-0.874347</td>\n",
              "      <td>0.223124</td>\n",
              "      <td>0.974790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-20</th>\n",
              "      <td>144.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2013</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>8.660254e-01</td>\n",
              "      <td>0.348202</td>\n",
              "      <td>0.93742</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.123234e-17</td>\n",
              "      <td>1.224647e-16</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.790776</td>\n",
              "      <td>-0.612106</td>\n",
              "      <td>0.339308</td>\n",
              "      <td>0.940675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-27</th>\n",
              "      <td>143.0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2013</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>8.660254e-01</td>\n",
              "      <td>0.456629</td>\n",
              "      <td>0.889657</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.123234e-17</td>\n",
              "      <td>1.224647e-16</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.724793</td>\n",
              "      <td>0.688967</td>\n",
              "      <td>0.450517</td>\n",
              "      <td>0.892768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-02-03</th>\n",
              "      <td>149.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2013</td>\n",
              "      <td>0.866025</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.558647</td>\n",
              "      <td>0.829406</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.123234e-17</td>\n",
              "      <td>1.224647e-16</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.571268</td>\n",
              "      <td>0.820763</td>\n",
              "      <td>0.555120</td>\n",
              "      <td>0.831770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-02-10</th>\n",
              "      <td>145.0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2013</td>\n",
              "      <td>0.866025</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.652822</td>\n",
              "      <td>0.757511</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.123234e-17</td>\n",
              "      <td>1.224647e-16</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.897805</td>\n",
              "      <td>-0.440394</td>\n",
              "      <td>0.651584</td>\n",
              "      <td>0.758576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-02-17</th>\n",
              "      <td>121.0</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>2013</td>\n",
              "      <td>0.866025</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.737833</td>\n",
              "      <td>0.674983</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.123234e-17</td>\n",
              "      <td>1.224647e-16</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.299363</td>\n",
              "      <td>-0.954139</td>\n",
              "      <td>0.738494</td>\n",
              "      <td>0.674260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-02-24</th>\n",
              "      <td>166.0</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>2013</td>\n",
              "      <td>0.866025</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.812487</td>\n",
              "      <td>0.582979</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.123234e-17</td>\n",
              "      <td>1.224647e-16</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.988468</td>\n",
              "      <td>0.151428</td>\n",
              "      <td>0.814576</td>\n",
              "      <td>0.580057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-03-03</th>\n",
              "      <td>160.0</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2013</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.123234e-17</td>\n",
              "      <td>0.875735</td>\n",
              "      <td>0.482792</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.123234e-17</td>\n",
              "      <td>1.224647e-16</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.571268</td>\n",
              "      <td>0.820763</td>\n",
              "      <td>0.878714</td>\n",
              "      <td>0.477349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-03-10</th>\n",
              "      <td>147.0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>2013</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.123234e-17</td>\n",
              "      <td>0.92669</td>\n",
              "      <td>0.375828</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.123234e-17</td>\n",
              "      <td>1.224647e-16</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.897805</td>\n",
              "      <td>-0.440394</td>\n",
              "      <td>0.929968</td>\n",
              "      <td>0.367642</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 28 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc1bd26a-63f3-4f24-b182-00a675e7f28c')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-bc1bd26a-63f3-4f24-b182-00a675e7f28c 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-bc1bd26a-63f3-4f24-b182-00a675e7f28c');\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",
              "\n",
              "\n",
              "<div id=\"df-31ea3d3a-2c92-48c6-a0f0-6ad5d80a1d5a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-31ea3d3a-2c92-48c6-a0f0-6ad5d80a1d5a')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-31ea3d3a-2c92-48c6-a0f0-6ad5d80a1d5a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "res_df"
            }
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "res_df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nC-cGdGQMZDb"
      },
      "outputs": [],
      "source": [
        "X_filtered_g_1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E5dFtkd9Ug1-"
      },
      "outputs": [],
      "source": [
        "X_filtered_g_1.columns.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[i for i in X_filtered_g_1.columns.tolist() if 'sum_values' in i]"
      ],
      "metadata": {
        "id": "jR1nrh39mimC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_filtered_g_1.loc['2013-03-31','lag_4__sum_values']\n",
        "# 2013-03-31  171.0  159.0  147.0  160.0"
      ],
      "metadata": {
        "id": "cX6hgbgAmzc6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import TimeSeriesSplit, cross_val_score\n",
        "from catboost import CatBoostRegressor\n",
        "import numpy as np\n",
        "\n",
        "# Генерация синтетических данных\n",
        "X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14]])\n",
        "y = np.array([1, 2, 3, 4, 5, 6, 7])\n",
        "\n",
        "# Создание модели\n",
        "model = CatBoostRegressor(verbose=False)\n",
        "\n",
        "# Значения n_splits для проверки\n",
        "n_splits_values = [2, 3, 4, 5,6]\n",
        "\n",
        "# Словарь для сохранения среднего значения оценок\n",
        "mean_scores = {}\n",
        "mean_scores_1 = []\n",
        "\n",
        "# Проход по всем значениям n_splits\n",
        "for n_splits in n_splits_values:\n",
        "    tscv = TimeSeriesSplit(n_splits=n_splits)\n",
        "    scores = cross_val_score(model, X, y, cv=tscv, scoring='neg_mean_squared_error')#'neg_mean_squared_error' 'r2'\n",
        "    mean_scores[n_splits] = np.mean(scores)\n",
        "    mean_scores_1.append(np.mean(scores))\n",
        "\n",
        "# Нахождение оптимального значения n_splits\n",
        "best_n_splits = max(mean_scores, key=mean_scores.get)\n",
        "print(f'Оптимальное значение n_splits: {best_n_splits}')\n",
        "print(mean_scores_1)\n",
        "# Построение графика\n",
        "plt.plot(n_splits_values, mean_scores_1, marker='o')\n",
        "plt.xlabel('n_splits')\n",
        "plt.ylabel('Neg Mean Squared Error')\n",
        "plt.title('n_splits vs Neg Mean Squared Error')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "YRUCyYYFnJvu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mSrkqja6NZYH"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPklAQBsSbrmR/osC45XQdk",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}