Time Series Forecasting Based on Wavelet KPCA and Support Vector Machine

Kernel principal components analysis (KPCA) has the advantage of extracting nonlinear features. Nonlinear mapping and generalization are the strong capabilities of support vector machine (SVM). By integrating the characteristics of KPCA and SVM, a chaotic time series forecasting method based on these two algorithms is presented. The wavelet is a kernel for KPCA and support vector machines, and genetic algorithm (GA) is used to tune the parameters automatically. It is shown that the proposed method in this paper has two-fold contributions: (1) this approach can escape from the blindness of man-made choice of the parameters. (2) The method possesses higher prediction precision and excellent forecasting effect.