Time Series Prediction Using Empirical Mode Decomposition and Least Squares Support Vector Machines

This article presents a MATLAB-based program designed for empirical mode decomposition (EMD) of time series data. The implementation goes beyond basic decomposition by incorporating least squares support vector machine (LS-SVM) prediction capabilities. The program employs the EMD algorithm to decompose complex time series into intrinsic mode functions (IMFs), which are then used as features for the LS-SVM regression model. This approach significantly enhances prediction accuracy by handling non-linear and non-stationary time series characteristics through the decomposition process. The MATLAB implementation likely utilizes key functions such as emd() for signal decomposition and a custom LS-SVM implementation using quadratic programming optimization for regression tasks. The program's architecture supports excellent extensibility, allowing users to modify parameters including decomposition levels, SVM kernel functions, and regularization parameters. The modular design facilitates customization for specific forecasting applications while maintaining computational efficiency. This integrated EMD-LS-SVM approach provides a powerful tool for improving time series forecasting accuracy, enabling better decision-making through more reliable trend predictions. The combination of signal processing techniques with machine learning algorithms makes this program particularly valuable for complex temporal pattern recognition and predictive analytics.