A new method of determining the optimal embedding dimension based on nonlinear prediction
Meng Qing-Fang(孟庆芳)a)†, Peng Yu-Hua(彭玉华)a), and Xue Pei-Jun(薛佩军)b)
a School of Information Science and Engineering, Shandong University, Jinan 250100, China; b Graduate School of Shandong University, Jinan 250100, China
Abstract A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive prediction model parameterized by the embedding dimension and the nonlinear degree. Simulation results show the effectiveness of this method. And this method is applicable to a short time series, stable to noise, computationally efficient, and without any purposely introduced parameters.
Received: 15 August 2006
Revised: 18 December 2006
Accepted manuscript online:
Fund: Project supported by the Scientific
Research Foundation for the Returned Overseas Chinese Scholars of
China (Grant No 2004.176.4) and the Natural Science Foundation of
Shandong Province of China (Grant No Z2004G01).
Cite this article:
Meng Qing-Fang(孟庆芳), Peng Yu-Hua(彭玉华), and Xue Pei-Jun(薛佩军) A new method of determining the optimal embedding dimension based on nonlinear prediction 2007 Chinese Physics 16 1252
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