Local Piecewise-Linearity Prediction Method of Chaotic Time Series

Juan Liu; Xue Wei Bai; Dao Cai Chi

doi:10.4028/www.scientific.net/AMR.712-715.2415

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 712-715Local Piecewise-Linearity Prediction Method of...

Local Piecewise-Linearity Prediction Method of Chaotic Time Series

Abstract:

A Local Piecewise-Linearity Prediction method is presented, Based on the advantages and limitations of local prediction of chaotic time series. Taking time series of rainfall as example for prediction the rainfall of one city in Liaoning province, which includes the application of the largest Lyapunov exponent, Local-region method and Local Piecewise-Linearity method. The method proposed is proved practical in comparison with the observed data.

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Periodical:

Advanced Materials Research (Volumes 712-715)

Pages:

2415-2418

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.712-715.2415

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Online since:

June 2013

Authors:

Juan Liu, Xue Wei Bai, Dao Cai Chi

Keywords:

Chaotic Time Series , Largest Lyapunov Exponent, Local Piecewise-Linearity Model, Local-Region Method

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References

[1] Jinhu Lv, Jinan Lu: Chaotic Time Series Analysis and Applying (Wuhan University press, China 2001).( In Chinese)

Google Scholar

[2] G.C. Philipatos, E.Pilarnu and A.G. Mailiiaris: Working paper (1993).

Google Scholar

[3] Wojcik R, Buishnad T A: Journal of Hydrology. Vol. 273 (2003), pp.69-80.

Google Scholar

[4] Liu D L, Scott B J: Agricultural and Forest Meteorology. Vol. 106 (2001), pp.41-59.

Google Scholar

[5] Jiashu Zhang: ACTA PHYSICA SINICA. Vol. 56 (2007), pp.0067-0077. (In Chinese)

Google Scholar

[6] Fanghong Xiao: ACTA PHYSICA SINICA.Vol. 54 (2005), pp.0550-0556. (In Chinese)

Google Scholar

[7] TianLiang Yao, HaiFeng Liu: ACTA PHYSICA SINICA. Vol. 61(2012), pp.373-376. (In Chinese)

Google Scholar

[8] Yong-Dao Zhou, Hong Ma: ACTA PHYSICA SINICA. Vol. 56(2007), pp.6809-6813. (In Chinese)

Google Scholar

[9] Jian-Chao Gan: ACTA PHYSICA SINICA. Vol. 52(2003), P.2995-3001. (In Chinese)

Google Scholar

[10] Xiaohua Yang: JOURANAL OF CATASTROPHOLOGY. Vol. 14(1999), pp.1-6. (In Chinese)

Google Scholar

[11] Jian Mao, Anlin Yao: Oil & Gas Storage and Transportation. Vol. 28(2009), pp.42-44. (In Chinese)

Google Scholar

[12] Changdong Li, Huiming Tang: Journal of Wuhan University of Technology. Vol. 30(2008), pp.133-135. (In Chinese)

Google Scholar