MATLAB Support Vector Machine Prediction with Implementation Guide

This article demonstrates how to implement Support Vector Machine (SVM) predictions using MATLAB. We provide raw datasets, detailed case analysis, and fully functional source code (personally debugged and verified, included in appendices). Before running the code, ensure the libsvm toolbox is properly installed. The case study centers on short-term electric power load forecasting, illustrating practical SVM application for real-world prediction scenarios. The implementation covers key steps including data preprocessing using MATLAB's normalization functions, SVM model training with kernel selection (RBF kernel implementation shown), and parameter optimization using grid search techniques. We further explain SVM fundamentals and demonstrate how to tune critical parameters like penalty factor C and kernel parameters to improve prediction accuracy. The code includes visualization components for result analysis using MATLAB's plotting functions. This guide aims to deepen understanding of SVM mechanics and facilitate practical implementation in engineering applications.