Time series prediction using RNN in multi-dimension embedding phase space

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

73 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1868-1873
Journal / PublicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume2
Publication statusPublished - 1998

Conference

Title1998 IEEE International Conference on Systems, Man, and Cybernetics
PlaceUnited States
CitySan Diego, CA
Period11 - 14 October 1998

Abstract

In this paper, a multi-dimension chaotic time series prediction method using Recurrent Neural Network (RNN) in embedding phase space is proposed. This method is to reconstruct a phase space based on the chaotic time series and then embed these data as the phase space points for the training of the RNN. The resultant RNN after the training will be served as the embedding phase space which is capable of recovering the predicted phase space point into time domain. Thus, the predicted chaotic time series data can be obtained. Numerical results have shown that the proposed method is simple, practical and effective in chaotic time series prediction.