Phase space reconstruction of nonlinear time series based on Kernel method

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

doi:10.1109/WCICA.2006.1713201

Phase space reconstruction of nonlinear time series based on Kernel method

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

^*Corresponding author for this work

Northeastern University China

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Pages	4364-4368
Number of pages	5
DOIs	https://doi.org/10.1109/WCICA.2006.1713201
Publication status	Published - 2006
Externally published	Yes
Event	6th World Congress on Intelligent Control and Automation, WCICA 2006 - Dalian, China Duration: 21 Jun 2006 → 23 Jun 2006

Publication series

Name	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Volume	1

Conference

Conference	6th World Congress on Intelligent Control and Automation, WCICA 2006
Country/Territory	China
City	Dalian
Period	21/06/06 → 23/06/06

Keywords

Correlation analysis
Kernel principal component analysis
Nonlinear time series
Phase space reconstruction

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10.1109/WCICA.2006.1713201

Cite this

Lin, S., Qiao, J., Wang, G., Zhang, S., & Zhi, L. (2006). Phase space reconstruction of nonlinear time series based on Kernel method. In Proceedings of the World Congress on Intelligent Control and Automation (WCICA) (pp. 4364-4368). Article 1713201 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 1). https://doi.org/10.1109/WCICA.2006.1713201

@inproceedings{06441ede1e1a4d0ca0a8b1cb3fcc034f,

title = "Phase space reconstruction of nonlinear time series based on Kernel method",

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 analysis, Kernel principal component analysis, Nonlinear time series, Phase space reconstruction",

author = "Shukuan Lin and Jianzhong Qiao and Guoren Wang and Shaomin Zhang and Lijia Zhi",

year = "2006",

doi = "10.1109/WCICA.2006.1713201",

language = "English",

isbn = "1424403324",

series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

pages = "4364--4368",

booktitle = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

note = "6th World Congress on Intelligent Control and Automation, WCICA 2006 ; Conference date: 21-06-2006 Through 23-06-2006",

}

Lin, S, Qiao, J, Wang, G, Zhang, S & Zhi, L 2006, Phase space reconstruction of nonlinear time series based on Kernel method. in Proceedings of the World Congress on Intelligent Control and Automation (WCICA)., 1713201, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), vol. 1, pp. 4364-4368, 6th World Congress on Intelligent Control and Automation, WCICA 2006, Dalian, China, 21/06/06. https://doi.org/10.1109/WCICA.2006.1713201

Phase space reconstruction of nonlinear time series based on Kernel method. / Lin, Shukuan; Qiao, Jianzhong; Wang, Guoren et al.
Proceedings of the World Congress on Intelligent Control and Automation (WCICA). 2006. p. 4364-4368 1713201 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Phase space reconstruction of nonlinear time series based on Kernel method

AU - Lin, Shukuan

AU - Qiao, Jianzhong

AU - Wang, Guoren

AU - Zhang, Shaomin

AU - Zhi, Lijia

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

KW - Correlation analysis

KW - Kernel principal component analysis

KW - Nonlinear time series

KW - Phase space reconstruction

UR - https://www.scopus.com/pages/publications/34047200578

U2 - 10.1109/WCICA.2006.1713201

DO - 10.1109/WCICA.2006.1713201

M3 - Conference contribution

AN - SCOPUS:34047200578

SN - 1424403324

SN - 9781424403325

T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

SP - 4364

EP - 4368

BT - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

T2 - 6th World Congress on Intelligent Control and Automation, WCICA 2006

Y2 - 21 June 2006 through 23 June 2006

ER -

Phase space reconstruction of nonlinear time series based on Kernel method

Abstract

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Conference

Keywords

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