{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "fv9OoQsMFk5A" }, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在本筆記中，我們將展示如何：\n", "\n", "- 準備二維時間序列數據以訓練 SVM 回歸模型\n", "- 使用 RBF 核實現 SVR\n", "- 通過圖表和 MAPE 評估模型\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 匯入模組\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": [ "## 準備資料\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8fywSjC6GsRz" }, "source": [ "### 加載數據\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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load
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" ], "text/plain": [ " load\n", "2012-01-01 00:00:00 2698.0\n", "2012-01-01 01:00:00 2558.0\n", "2012-01-01 02:00:00 2444.0\n", "2012-01-01 03:00:00 2402.0\n", "2012-01-01 04:00:00 2403.0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "energy = load_data('../../data')[['load']]\n", "energy.head(5)" ] }, { "cell_type": "markdown", "metadata": { "id": "O0BWP13rGnh4" }, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 486 }, "id": "hGaNPKu_Gidk", "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d" }, "outputs": [], "source": [ "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n", "plt.xlabel('timestamp', fontsize=12)\n", "plt.ylabel('load', fontsize=12)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "IPuNor4eGwYY" }, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ysvsNyONGt0Q" }, "outputs": [], "source": [ "train_start_dt = '2014-11-01 00:00:00'\n", "test_start_dt = '2014-12-30 00:00:00'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 548 }, "id": "SsfdLoPyGy9w", "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7" }, "outputs": [], "source": [ "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n", " .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n", " .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n", "plt.xlabel('timestamp', fontsize=12)\n", "plt.ylabel('load', fontsize=12)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "XbFTqBw6G1Ch" }, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "現在，您需要通過過濾和縮放數據來準備訓練所需的數據。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cYivRdQpHDj3", "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1" }, "outputs": [], "source": [ "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n", "test = energy.copy()[energy.index >= test_start_dt][['load']]\n", "\n", "print('Training data shape: ', train.shape)\n", "print('Test data shape: ', test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "將數據縮放至範圍 (0, 1)。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "3DNntGQnZX8G", "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c" }, "outputs": [], "source": [ "scaler = MinMaxScaler()\n", "train['load'] = scaler.fit_transform(train)\n", "train.head(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "26Yht-rzZexe", "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301" }, "outputs": [], "source": [ "test['load'] = scaler.transform(test)\n", "test.head(5)" ] }, { "cell_type": "markdown", "metadata": { "id": "x0n6jqxOQ41Z" }, "source": [ "### 使用時間步驟創建數據\n" ] }, { "cell_type": "markdown", "metadata": { "id": "fdmxTZtOQ8xs" }, "source": [ "對於我們的 SVR，我們將輸入數據轉換為 `[batch, timesteps]` 的形式。因此，我們重新調整現有的 `train_data` 和 `test_data`，使其具有一個新的維度，該維度表示時間步。在我們的例子中，我們取 `timesteps = 5`。因此，模型的輸入是前 4 個時間步的數據，輸出將是第 5 個時間步的數據。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Rpju-Sc2HFm0" }, "outputs": [], "source": [ "# Converting to numpy arrays\n", "\n", "train_data = train.values\n", "test_data = test.values" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Selecting the timesteps\n", "\n", "timesteps=None" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "O-JrsrsVJhUQ", "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef" }, "outputs": [], "source": [ "# Converting data to 2D tensor\n", "\n", "train_data_timesteps=None" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "exJD8AI7KE4g", "outputId": "ce90260c-f327-427d-80f2-77307b5a6318" }, "outputs": [], "source": [ "# Converting test data to 2D tensor\n", "\n", "test_data_timesteps=None" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2u0R2sIsLuq5" }, "outputs": [], "source": [ "x_train, y_train = None\n", "x_test, y_test = None\n", "\n", "print(x_train.shape, y_train.shape)\n", "print(x_test.shape, y_test.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "8wIPOtAGLZlh" }, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EhA403BEPEiD" }, "outputs": [], "source": [ "# Create model using RBF kernel\n", "\n", "model = None" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GS0UA3csMbqp", "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d" }, "outputs": [], "source": [ "# Fit model on training data" ] }, { "cell_type": "markdown", "metadata": { "id": "Rz_x8S3UrlcF" }, "source": [ "### 建立模型預測\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XR0gnt3MnuYS", "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364" }, "outputs": [], "source": [ "# Making predictions\n", "\n", "y_train_pred = None\n", "y_test_pred = None" ] }, { "cell_type": "markdown", "metadata": { "id": "_2epncg-SGzr" }, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Scaling the predictions\n", "\n", "y_train_pred = scaler.inverse_transform(y_train_pred)\n", "y_test_pred = scaler.inverse_transform(y_test_pred)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xmm_YLXhq7gV", "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1" }, "outputs": [], "source": [ "# Scaling the original values\n", "\n", "y_train = scaler.inverse_transform(y_train)\n", "y_test = scaler.inverse_transform(y_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "u3LBj93coHEi", "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4" }, "outputs": [], "source": [ "# Extract the timesteps for x-axis\n", "\n", "train_timestamps = None\n", "test_timestamps = None" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(25,6))\n", "# plot original output\n", "# plot predicted output\n", "plt.legend(['Actual','Predicted'])\n", "plt.xlabel('Timestamp')\n", "plt.title(\"Training data prediction\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LnhzcnYtXHCm", "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b" }, "outputs": [], "source": [ "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 225 }, "id": "53Q02FoqQH4V", "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583" }, "outputs": [], "source": [ "plt.figure(figsize=(10,3))\n", "# plot original output\n", "# plot predicted output\n", "plt.legend(['Actual','Predicted'])\n", "plt.xlabel('Timestamp')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "clOAUH-SXCJG", "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5" }, "outputs": [], "source": [ "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')" ] }, { "cell_type": "markdown", "metadata": { "id": "DHlKvVCId5ue" }, "source": [ "## 全數據集預測\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cOFJ45vreO0N", "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16" }, "outputs": [], "source": [ "# Extracting load values as numpy array\n", "data = None\n", "\n", "# Scaling\n", "data = None\n", "\n", "# Transforming to 2D tensor as per model input requirement\n", "data_timesteps=None\n", "\n", "# Selecting inputs and outputs from data\n", "X, Y = None, None" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ESSAdQgwexIi" }, "outputs": [], "source": [ "# Make model predictions\n", "\n", "# Inverse scale and reshape\n", "Y_pred = None\n", "Y = None" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 328 }, "id": "M_qhihN0RVVX", "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80" }, "outputs": [], "source": [ "plt.figure(figsize=(30,8))\n", "# plot original output\n", "# plot predicted output\n", "plt.legend(['Actual','Predicted'])\n", "plt.xlabel('Timestamp')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AcN7pMYXVGTK", "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770" }, "outputs": [], "source": [ "print('MAPE: ', mape(Y_pred, Y)*100, '%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n---\n\n**免責聲明**： \n本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們努力確保翻譯的準確性，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於關鍵信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "Recurrent_Neural_Networks.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "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": "3.7.1" }, "coopTranslator": { "original_hash": "e86ce102239a14c44585623b9b924a74", "translation_date": "2025-09-03T20:01:36+00:00", "source_file": "7-TimeSeries/3-SVR/working/notebook.ipynb", "language_code": "tw" } }, "nbformat": 4, "nbformat_minor": 1 }