SVM for Time Series Forecasting

Support Vector Machine (SVM) application in time series forecasting represents a powerful machine learning approach, particularly suitable for handling nonlinear patterns, small-sample datasets, and high-dimensional prediction problems. Within the MATLAB environment, SVM can be implemented to construct various time series forecasting models, including univariate prediction, multivariate prediction, and dynamic forecasting models incorporating sliding window techniques.

SVM utilizes kernel functions (such as RBF, linear, and polynomial kernels) to map data into high-dimensional spaces, enabling the identification of optimal hyperplanes that capture time series trends and periodicity. For time series data, the critical aspect lies in feature engineering construction, particularly through incorporating lag features where historical data points serve as input variables for predicting future values.

MATLAB provides functions like fitrsvm for regression-based SVM training, allowing flexible parameter adjustments (including penalty coefficient C and kernel parameters) to optimize forecasting performance. Furthermore, SVM can be integrated with models like ARIMA and LSTM to form hybrid forecasting frameworks, enhancing long-term prediction robustness through complementary algorithmic strengths.