Phase Space Reconstruction of Nonlinear Time Series Based on Kernel Method

Shukuan Lin Shukuan Lin,Jianzhong Qiao Jianzhong Qiao,Lijia Zhi Lijia Zhi,Shaomin Zhang Shaomin Zhang,Guoren Wang Guoren Wang

doi:10.1109/wcica.2006.1713201

Phase Space Reconstruction of Nonlinear Time Series Based on Kernel Method

Shukuan Lin Shukuan Lin, Jianzhong Qiao Jianzhong Qiao + Show 3 more

https://doi.org/10.1109/wcica.2006.1713201

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Publication Date: Jan 1, 2006

Citations: 10

Affiliation: Northeastern University

#Kernel Principal Component Analysis #Kernel Principal Component + Show 8 more

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Abstract

A phase space reconstruction method KPCA-CA was proposed based on Kernel Principal Component Analysis (KPCA) and Correlation Analysis (CA) for nonlinear time series. On the basis of KPCA, the correlation was analyzed between every kernel principal component and output variable, and some kernel principal components were discontinuously chosen according to their correlation degree to form the phase space of nonlinear time series. The method was compared with other methods of phase space reconstruction. The experimental results show that modeling accuracy for nonlinear time series is highest based on the phase space reconstruction method proposed by the paper, proving the efficiency of the method.

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