SVM for Time Series Forecasting with MATLAB Implementation

This document presents a MATLAB-implemented Support Vector Machine (SVM) program designed for time series forecasting applications. The implementation incorporates multiple time series prediction models, providing comprehensive examples to facilitate better understanding and practical application of these methodologies. The program utilizes SVM regression techniques (typically via fitrsvm function) with appropriate kernel selections (RBF, linear, or polynomial) to handle temporal dependencies in sequential data. Key preprocessing steps include time lag feature engineering, data normalization, and cross-validation setup to ensure robust model performance. SVM-based time series forecasting delivers accurate results across various data types, making it suitable for financial market analysis, sales prediction, energy demand forecasting, and other domain-specific applications. The code structure includes modular components for data partitioning, model training, hyperparameter optimization (using Bayesian optimization or grid search), and performance evaluation metrics (RMSE, MAE). This implementation aims to support reliable and precise time series analysis for both research and practical deployment scenarios, offering customizable parameters to adapt to different forecasting requirements.