Determining the minimum embedding dimension of nonlinear time series based on prediction method

Determining the minimum embedding dimension of nonlinear time series based on prediction

method

Bian Chun-Hua;Ning Xin-Bao

【期刊名称】《中国物理：英文版》 【年(卷),期】2004(013)005

【摘要】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. 【总页数】4页(633-636)

【关键词】nonlinear time series, embedding dimension, NAR model, prediction5. Conclusion

【作者】Bian Chun-Hua;Ning Xin-Bao

【作者单位】State Key Laboratory of Modern Acoustics, Institute for Biomedical Electronic Engineering, Nanjing University, Nanjing 210093,

China;State Key Laboratory of Modern Acoustics, Institute for Biomedical Electronic Engineering, Nanjing University, Nanjing 210093, China

【正文语种】中文 【中图分类】O4 【相关文献】

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