Integrating kernel principal component analysis with least squares support vector machines for time series forecasting problems

GUO Hui; WANG Ling; LIU Heping

doi:10.13374/j.issn1001-053x.2006.03.022

Volume 28 Issue 3

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GUO Hui, WANG Ling, LIU Heping. Integrating kernel principal component analysis with least squares support vector machines for time series forecasting problems[J]. Chinese Journal of Engineering, 2006, 28(3): 303-306. doi: 10.13374/j.issn1001-053x.2006.03.022

Citation:

GUO Hui, WANG Ling, LIU Heping. Integrating kernel principal component analysis with least squares support vector machines for time series forecasting problems[J]. Chinese Journal of Engineering, 2006, 28(3): 303-306. doi: 10.13374/j.issn1001-053x.2006.03.022

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Integrating kernel principal component analysis with least squares support vector machines for time series forecasting problems

doi: 10.13374/j.issn1001-053x.2006.03.022

Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China

Received Date: 2005-01-24
Rev Recd Date: 2005-11-17

Available Online: 2021-08-24

Abstract

Abstract

This paper discusses least squares support vector machines (LSSVM) in the time series forecasting problem. Kernel principal component analysis (KPCA) is proposed to calculate principal component. Least squares support vector machines are applied to predict time series. Experimental results show that the performance of LSSVM with feature extraction using KPCA is much better than that without feature extraction. In comparison with PCA, there is also superior Derforrnance in KPCA.