Time Series Forecasting Based on Wavelet KPCA and Support Vector Machine

摘要：

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.

关键词： wavelet kernel kernel principal component analysis support vector machine wavelet kernel principal component analysis

作者: Fei Chen Chongzhao Han

作者单位: School of Electronic & Information Engineering Xian Jiaotong University Xian, Shaanxi Province, China

会议类型: 国际会议

会议名称: 2007 IEEE International Conference on Automation and Lofistics

会议地点: 山东济南

会议语种:英文

在线出版日期: 2007-08-18（万方平台首次上网日期，不代表论文的发表时间）