{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "fv9OoQsMFk5A" }, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "В этом блокноте мы демонстрируем, как:\n", "\n", "- подготовить двумерные временные ряды для обучения модели регрессора SVM\n", "- реализовать SVR с использованием RBF ядра\n", "- оценить модель с помощью графиков и MAPE\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Импорт модулей\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append('../../')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "M687KNlQFp0-" }, "outputs": [], "source": [ "import os\n", "import warnings\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import datetime as dt\n", "import math\n", "\n", "from sklearn.svm import SVR\n", "from sklearn.preprocessing import MinMaxScaler\n", "from common.utils import load_data, mape" ] }, { "cell_type": "markdown", "metadata": { "id": "Cj-kfVdMGjWP" }, "source": [] }, { "cell_type": "markdown", "metadata": { "id": "8fywSjC6GsRz" }, "source": [ "### Загрузить данные\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "aBDkEB11Fumg", "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740" }, "outputs": [ { "data": { "text/html": [ "
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2012-01-01 00:00:00 | \n", "2698.0 | \n", "
2012-01-01 01:00:00 | \n", "2558.0 | \n", "
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2012-01-01 04:00:00 | \n", "2403.0 | \n", "