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

被引：0
|
作者：
Bian, CH [1 ]
Ning, XB [1 ]
机构：
[1] Nanjing Univ, State Key Lab Modern Acoust, Inst Biomed Elect Engn, Nanjing 210093, Peoples R China
来源：
CHINESE PHYSICS | 2004年 / 13卷 / 05期
关键词：
nonlinear time series; embedding dimension; NAR model; prediction;
D O I：
暂无
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
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.
引用
收藏
页码：633 / 636
页数：4
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