Phase Space Reconstruction of Nonlinear Time Series Based on Kernel Method

Author

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

Author_Institution

Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang

Volume

1

fYear

0

Firstpage

4364

Lastpage

4368

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

Keywords

correlation methods; phase space methods; principal component analysis; time series; correlation analysis; kernel principal component analysis; nonlinear time series; phase space reconstruction; Delay effects; Educational institutions; Information analysis; Information science; Kernel; Nonlinear dynamical systems; Predictive models; Principal component analysis; Reconstruction algorithms; Time series analysis; Correlation analysis; Kernel principal component analysis; Nonlinear time series; Phase space reconstruction;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713201

Filename

1713201