{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "7669f948-8d8e-460f-a6b4-e4358db9ec58", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", "from statsmodels.tsa.holtwinters import ExponentialSmoothing" ] }, { "cell_type": "markdown", "id": "b0fa9e7b-c123-4b1e-8918-2397e77429c8", "metadata": {}, "source": [ "Определим функцию сглаживания временного ряда, она понадобится позже.\n", "\n", "Например:\n", "1. Метод скользящего окна. Параметр window задает ширину окна усреднения значений.\n", "\n", " ``return ts.rolling(window=5).mean()``\n", "\n", "2. Метод экспоненциального сглаживания (метод Хольта-Уинтерса)\n", "\n", "\n", " варианты для параметра trend: \"add\", \"mul\", \"additive\", \"multiplicative\", None\n", "\n", "\n", " варианты для параметра seasonal: \"add\", \"mul\", \"additive\", \"multiplicative\", None\n", "\n", "\n", " seasonal_periods задает для модели предполагаемый интервал сезонности, когда ВР будет иметь похожие уровни и тенденции\n", "\n", " ``model = ExponentialSmoothing(ts, trend=\"additive\", seasonal=\"additive\", seasonal_periods=5)``\n", " ``return model.fit().fittedvalues``" ] }, { "cell_type": "code", "execution_count": 2, "id": "b1525171-5f8b-410e-8b04-599757b1afe1", "metadata": {}, "outputs": [], "source": [ "def smooth_time_series(ts):\n", " model = ExponentialSmoothing(ts, trend=\"additive\", seasonal=\"additive\", seasonal_periods=5)\n", " return model.fit().fittedvalues" ] }, { "cell_type": "markdown", "id": "6e2859e5-739e-4b49-8999-4a54aeebb903", "metadata": {}, "source": [ "Для чтения из файла необходимо взять временные ряды из предложенных наборов и сохранить в csv файл как показано в примере, сформировав датасет" ] }, { "cell_type": "code", "execution_count": 3, "id": "64bdc662-a64e-43be-a3d6-f4327b29f477", "metadata": {}, "outputs": [], "source": [ "dataset = pd.read_csv(\"ts2.csv\", delimiter=\";\")" ] }, { "cell_type": "markdown", "id": "345981a3-8b6d-42dc-b5ab-aed2bdcce8d8", "metadata": {}, "source": [ "после того как файл был прочтен, можно вывести содержимое временных рядов в консоль" ] }, { "cell_type": "code", "execution_count": 4, "id": "c228b5b9-5bfb-4786-887d-857d9b3409c7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " first second third\n", "0 3 2.500000 1\n", "1 2 2.833333 2\n", "2 3 2.966667 3\n", "3 3 3.066667 4\n", "4 4 4.383333 5\n", "5 6 6.466667 6\n", "6 5 5.416667 7\n", "7 5 5.433333 8\n", "8 5 5.583333 9\n", "9 5 5.683333 10\n", "10 5 5.150000 11\n", "11 5 5.633333 12\n", "12 6 6.033333 13\n", "13 12 12.866667 14\n", "14 6 6.783333 15\n", "15 6 6.500000 16\n" ] } ], "source": [ "print(dataset)" ] }, { "cell_type": "markdown", "id": "e53b79b2-74e0-492d-bcab-5f3c47c25175", "metadata": {}, "source": [ "Для выполнения первой части задания необходимо подобрать метод и параметры сглаживания временного ряда. Для этого выше реализован пример функции ``smooth_time_series``" ] }, { "cell_type": "code", "execution_count": 5, "id": "74674c95-9af6-4b5a-ab08-30fe89c4e05e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sds = dataset.apply(smooth_time_series)\n", "\n", "plt.plot(sds)\n", "plt.plot(dataset)\n", "plt.legend(['sm first', 'sm second', 'sm third', 'first', 'second', 'third'])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "01e366c4-d7c0-4a01-85b7-f24c4383766d", "metadata": {}, "source": [ "Выполним нормализацию. Варианты: MinMaxScaler; MaxAbsScaler; RobustScaler; StandardScaler" ] }, { "cell_type": "code", "execution_count": 6, "id": "e609b470-19b7-4f52-af0d-b2643e926779", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " first second third\n", "0 0.032607 0.000000 0.000000\n", "1 0.000000 0.057355 0.066665\n", "2 0.113045 0.085473 0.133331\n", "3 0.452163 0.447628 0.199999\n", "4 0.169570 0.209032 0.266668\n", "5 0.306525 0.276186 0.333334\n", "6 0.273918 0.333541 0.399998\n", "7 0.386964 0.361659 0.466664\n", "8 0.726082 0.723814 0.533332\n", "9 0.443488 0.485218 0.600001\n", "10 0.580444 0.552372 0.666667\n", "11 0.547837 0.609727 0.733331\n", "12 0.660882 0.637845 0.799997\n", "13 1.000000 1.000000 0.866665\n", "14 0.717407 0.761404 0.933334\n", "15 0.854362 0.828558 1.000000\n" ] } ], "source": [ "df = pd.DataFrame(MinMaxScaler().fit_transform(sds), columns=sds.columns)\n", "print(df)" ] }, { "cell_type": "markdown", "id": "8d7324a5-0610-4ac6-b270-1f0eedf00c2f", "metadata": {}, "source": [ "Расчет степени корреляции между сглаженными временными рядами" ] }, { "cell_type": "code", "execution_count": 7, "id": "5e1850c7-17c7-49ae-9b71-594934054b03", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " first second third\n", "first 1.000000 0.992092 0.904237\n", "second 0.992092 1.000000 0.916556\n", "third 0.904237 0.916556 1.000000\n" ] } ], "source": [ "matrix = df.corr()\n", "print(matrix)" ] }, { "cell_type": "markdown", "id": "cbe8bb83-5958-4e9c-8963-ad04d5653eb8", "metadata": {}, "source": [ "Для примера сравним корреляцию для несглаженных временных рядов" ] }, { "cell_type": "code", "execution_count": 8, "id": "07268810-4221-49b0-8c59-a07470f15a4e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " first second third\n", "first 1.000000 0.990651 0.704796\n", "second 0.990651 1.000000 0.722877\n", "third 0.704796 0.722877 1.000000\n" ] } ], "source": [ "matrix2 = dataset.corr()\n", "print(matrix2)" ] }, { "cell_type": "markdown", "id": "4c5a24e1-0734-4aaa-9212-56eadadd273f", "metadata": {}, "source": [ "Визуализация матрицы корреляции:" ] }, { "cell_type": "code", "execution_count": 9, "id": "d3e09b25-63ec-4c52-9013-19f4be727ffb", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(matrix, cmap='Blues')\n", "plt.colorbar()\n", "variables = []\n", "for i in matrix.columns:\n", " variables.append(i)\n", "plt.xticks(range(len(matrix)), variables, rotation=45, ha='right')\n", "plt.yticks(range(len(matrix)), variables)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 5 }