SVM for Time Series Forecasting with MATLAB Implementation

This SVM program implemented in MATLAB is designed for time series forecasting applications. The MATLAB programming environment provides robust tools for implementing Support Vector Machine algorithms that can effectively analyze sequential data patterns. The program employs key SVM functions such as fitrsvm for regression tasks or fitcsvm for classification approaches, utilizing kernel methods like RBF (Radial Basis Function) to capture temporal dependencies in time series data. Implementation involves feature engineering techniques including lag variable creation, rolling window statistics, and seasonal decomposition to enhance prediction accuracy. The algorithm works by mapping time series data into higher-dimensional feature spaces where linear regression becomes feasible, employing structural risk minimization principles to prevent overfitting. Critical implementation aspects include parameter optimization through cross-validation techniques, selection of appropriate kernel functions, and handling of temporal autocorrelation. This approach requires programming proficiency in MATLAB's Machine Learning Toolbox and understanding of time series fundamentals like stationarity, trend analysis, and seasonal adjustments. The MATLAB implementation offers advantages through built-in functions for data preprocessing, model validation, and performance metrics calculation, making it an effective solution for time series prediction tasks.