中国物理B ›› 2007, Vol. 16 ›› Issue (5): 1252-1257.doi: 10.1088/1009-1963/16/5/014

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A new method of determining the optimal embedding dimension based on nonlinear prediction

薛佩军1, 孟庆芳2, 彭玉华2   

  1. (1)Graduate School of Shandong University, Jinan 250100, China; (2)School of Information Science and Engineering, Shandong University, Jinan 250100, China\
  • 收稿日期:2006-08-15 修回日期:2006-12-18 出版日期:2007-05-20 发布日期:2007-05-20
  • 基金资助:
    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).

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)   

  1. a School of Information Science and Engineering, Shandong University, Jinan 250100, China; b Graduate School of Shandong University, Jinan 250100, China
  • Received:2006-08-15 Revised:2006-12-18 Online:2007-05-20 Published:2007-05-20
  • Supported by:
    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).

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摘要： 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.

关键词: embedding dimension, nonlinear autoregressive prediction model, nonlinear time series

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.

Key words: embedding dimension, nonlinear autoregressive prediction model, nonlinear time series

中图分类号:  (Time series analysis)

  • 05.45.Tp
02.40.Pc (General topology) 05.10.-a (Computational methods in statistical physics and nonlinear dynamics) 05.40.Ca (Noise)