{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "fv9OoQsMFk5A" }, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在這份筆記中，我們將示範如何：\n", "\n", "- 準備二維時間序列數據以訓練 SVM 回歸模型\n", "- 使用 RBF 核心實現 SVR\n", "- 使用圖表和 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": [ "## 準備數據\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，您需要通過對數據進行篩選和縮放來準備訓練數據。\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": [ "### 建立具有時間步驟的數據\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，該維度表示時間步長。在我們的例子中，我們取 `timesteps = 5`。因此，模型的輸入是前 4 個時間步長的數據，輸出將是第 5 個時間步長的數據。\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": [ "### 進行模型預測\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本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，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。對於重要資訊，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錯誤解釋概不負責。\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": "2025-09-03T20:01:15+00:00", "source_file": "7-TimeSeries/3-SVR/working/notebook.ipynb", "language_code": "hk" } }, "nbformat": 4, "nbformat_minor": 1 }