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Multivariate chaotic time series phase space reconstruction based on extending dimension by conditional entropy

Ma Qian-Li Peng Hong Jiang You-Yi Zhang Chun-Tao

Multivariate chaotic time series phase space reconstruction based on extending dimension by conditional entropy

Ma Qian-Li, Peng Hong, Jiang You-Yi, Zhang Chun-Tao
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  • Abstract views:  4513
  • PDF Downloads:  1002
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  • Received Date:  12 April 2010
  • Accepted Date:  22 May 2010
  • Published Online:  05 January 2011

Multivariate chaotic time series phase space reconstruction based on extending dimension by conditional entropy

  • 1. (1)College of Computer and Engineering, South China University of Technology, Guangzhou 510006, China; (2)College of Mathematic and Computer Science, Chongqing Three Gorges University, Wanzhou 404000, China; (3)College of Mathematic and Computer Science, Chongqing Three Gorges University, Wanzhou 404000, China; College of Computer and Engineering, South China University of Technology, Guangzhou 510006, China

Abstract: For multivariate chaotic time series, a method of conditional entropy extending dimension(CEED) in the reconstructed phase space is proposed. First, the delay time of any variable time series is selected by mutual information method, and then the embedding dimension of phase space is extended by the conditional entropy. This method can ensure the independence of reconstructed coordinates from low space to high space and eliminate the redundancy of phase space, because the largest condition entropy is choosen. The effective input vector for the prediction of multivariate time series is given. Simulations of the Lorenz system and Henon system show that the neural network predictions of multivariate time series are much better than the prediction of univariate and existing multivariate. Therefore, CEED is effective for multivariate chaotic systems.

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