Time Series Prediction based on Wavelet Least Square Support Vector Machine

A chaotic time series prediction method based on the least square support vector machine (LS-SVM) with wavelet kernel is proposed in this paper. This method can approximate arbitrary functions, and is especially suitable for local processing, then improve the generalization ability of LS-SVM. The method is applied to Mackey-Glass and Lorenz equations, Henon mapping which produce the chaotic time series to evaluate the validity of the proposed technique based on the phase space reconstruction theory. Numerical experimental results confirm that the proposed method can predict the chaotic time series more effectively and accurately when compared with the existing prediction methods.