%0 Journal Article
%T Determining the minimum embedding dimension of nonlinear time series based on prediction method
%A Bian Chun-Hua
%A Ning Xin-Bao
%A <br>卞春华
%A 宁新宝
%J 中国物理 B
%D 2004
%I 
%X 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.
%K nonlinear time series
%K embedding dimension
%K NAR model
%K prediction<br>非线性时间级数
%K 嵌入尺寸
%K NAR模型
%K 物理声学
%K 声波传播
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=47EA7CFDDEBB28E0&jid=CD8D6A6897B9334F09D8D1648C376FB4&aid=72B7342159EA0D092558D8608F4E589F&yid=D0E58B75BFD8E51C&vid=FC0714F8D2EB605D&iid=94C357A881DFC066&sid=06F643376BC2509E&eid=9BF3B0483F192149&journal_id=1009-1963&journal_name=中国物理&referenced_num=1&reference_num=15