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Chin. Phys., 2004, Vol. 13(5): 633-636    DOI: 10.1088/1009-1963/13/5/012
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Determining the minimum embedding dimension of nonlinear time series based on prediction method

Bian Chun-Hua, Ning Xin-Bao
State Key Laboratory of Modern Acoustics, Institute for Biomedical Electronic Engineering, Nanjing University, Nanjing 210093, China
Abstract  Determining the embedding dimension of nonlinear time series plays an important role in the reconstruction of nonlinear dynamics. The paper first summarizes the current methods for determining the embedding dimension. Then, inspired by the fact that the optimum modelling dimension of nonlinear autoregressive (NAR) prediction model can characterize the embedding feature of the dynamics, the paper presents a new idea that the optimum modelling dimension of the NAR model can be taken as the minimum embedding dimension. Some validation examples and results are given and the present method shows its advantage for short data series.
Keywords:  NAR model      prediction      nonlinear time series      embedding dimension  
Received:  12 August 2003      Revised:  24 September 2003      Published:  06 July 2005
PACS:  05.45.Tp (Time series analysis)  
  05.45.Pq (Numerical simulations of chaotic systems)  

Cite this article: 

Bian Chun-Hua, Ning Xin-Bao Determining the minimum embedding dimension of nonlinear time series based on prediction method 2004 Chin. Phys. 13 633

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