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

页码：633 / 636

页数：4

共 50 条

[31] Phase space reconstruction of nonlinear time series based on kernel method
Lin, Shukuan
Qiao, Jianzhong
Wang, Guoren
Zhang, Shaomin
Zhi, Lijia
WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 4364 - +
[32] Effects of propofol anesthesia on nonlinear properties of EEG: Time-lag and embedding dimension
Roca-Gonzalez, J.
Vallverdu-Ferrer, M.
Caminal-Magrans, P.
Martinez-Gonzalez, F.
Roca-Dorda, J.
Alvarez-Gomez, J. A.
4TH EUROPEAN CONFERENCE OF THE INTERNATIONAL FEDERATION FOR MEDICAL AND BIOLOGICAL ENGINEERING, 2009, 22 (1-3): : 1268 - 1271
[33] Combination prediction method of chaotic time series
ZHAO DongHua
ChineseScienceBulletin, 2007, (04) : 570 - 573
[34] Combination prediction method of chaotic time series
Zhao DongHua
Ruan Jiong
Cai ZhiJie
CHINESE SCIENCE BULLETIN, 2007, 52 (04): : 570 - 573
[35] A Chaotic Prediction Method to Time Series Data
Liu, Binsheng
Weng, Ying
Hu, Xueping
ENVIRONMENT MATERIALS AND ENVIRONMENT MANAGEMENT PTS 1-3, 2010, 113-116 : 1367 - +
[36] Prediction for noisy nonlinear time series by echo state network based on dual estimation
Sheng, Chunyang
Zhao, Jun
Liu, Ying
Wang, Wei
NEUROCOMPUTING, 2012, 82 : 186 - 195
[37] Embedding dimension estimation of high dimensional chaotic time series using distributed time delay neural network
Parizangeneh, Maryam}
Ataei, Mohammad
Moallem, Peyman
PROCEEDINGS OF THE 8TH WSEAS INTERNATIONAL CONFERENCE ON SYSTEMS THEORY AND SCIENTIFIC COMPUTATION (ISTAC'08): NEW ASPECTS OF SYSTEMS THEORY AND SCIENTIFIC COMPUTATION, 2008, : 284 - +
[38] Modeling time series based fault prediction for model-unknown nonlinear system
Zhang, ZD
Hu, SS
DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES A-MATHEMATICAL ANALYSIS, 2006, 13 : 1641 - 1649
[39] Prediction of Chaotic Time Series Based on Incremental Method For Bayesian Network Learning
Li Chun-ying
Yang You-long
Zhang Heng-wei
PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE, 2012, : 4245 - 4249
[40] On the application of non-stationary time series prediction based on the SVM method
Wang Ge-Li
Yang Pei-Cai
Mao Yu-Qing
ACTA PHYSICA SINICA, 2008, 57 (02) : 714 - 719

← 1 2 3 4 5 →