Weighted least squares support vector machine local region method for nonlinear time series prediction

Authors:
Tingwei Quan;Xiaomao Liu;Qian Liu
Affiliations:
Department of Mathematics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China and Department of Mathematics, Hubei University of Normal, Wuhan, Hubei 430073, PR China;Department of Mathematics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China;Hubei Bioinformatics and Molecular Imaging Key Laboratory, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, PR China
Venue:
Applied Soft Computing
Year:
2010

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Abstract

For the prediction of nonlinear time series, weighted least squares support vector machine (WLS-SVM) local region method is proposed in this paper. The method has the following two advantages. First, the WLS-SVM can obtain robust estimates for regression through the limited observation, and in the WLS-SVM framework, there is a simple and efficient approach to model parameters selection based on leave-one-out cross-validation. Second, considering the estimate of the given point, using all samples is unnecessary. Training a segment of samples, which are familiar with the given point, can achieve high quality precise. Our method has been tried for prediction on two synthetic and the neuronal data sets. The results show that the method has more superior performance than other methods like LS-SVM.