Chaotic Time Series Prediction Based on Fuzzy Possibility C-mean and Composite Kernel Support Vector Regression
A clustering based composite kernels support vector machine ensemble forecasting model is proposed for the chaotic time series prediction. First, fuzzy possibility cmean clustering algorithm (FPCM) is adopted to partition the input dataset into several subsets, which can overcome the drawback caused by outlier and noise in conventional fuzzy cmean method. Then, SVMs with composite kernels that best fit partitioned subsets are constructed respectively, which hyperparameters are adaptively evolved by immune clone selection algorithm (ICGA). Finally, a fuzzy synthesis algorithm is employed to combine the outputs of submodels to obtain the final output, in which the degrees of memberships are generated by the relationship between a new input sample data and each subset center. Simulation results on a chaotic benchmark time series indicate that the presented algorithm shows good prediction performance compared to the other existing algorithms for the time series prediction task considered in this paper.
FPCM clustering algorithm SVM ensemble composite kernels ICGA chaotic time series prediction
Huizhi Yang Hui Ma
Zhongshan InstituteUniversity of Electronic Science and Technology ofChinaZhongshan, China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan, China
国际会议
成都
英文
1-4
2010-04-16（万方平台首次上网日期，不代表论文的发表时间）