The combining kernel PCA with PSO-SVM for chaotic time series prediction model

Chaotic time series analysis or forecasting is an important and complex problem in machine learning. As an effective tool, support vector machine (SVM) has been broadly adopted in pattern recognition and machine learning fields. In developing a successful SVM classifier, eliminating noise and extracting feature are very important. This paper proposes the application of kernel PCA to LS-SVM for feature extraction. Then PSO algorithm is employed to optimization of these parameters in LS-SVM. The novel chaotic time series analysis model integrates the advantages of wavelet transform, KPCA, PSO and LS-SVM. Compared with other predictors, this model has greater generality ability and higher accuracy.