Non-uniform attractor embedding for time series forecasting by fuzzy inference systems

A new method for identification of an optimal set of time lags based on non-uniform attractor embedding from the observed non-linear time series is proposed in this paper. Simple deterministic method for the determination of non-uniform time lags comprises the pre-processing stage of the time series...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2009-06, Vol.72 (10), p.2618-2626
Main Authors: Ragulskis, Minvydas, Lukoseviciute, Kristina
Format: Article
Language:English
Subjects:
Attractor embedding
Fuzzy inference system
Time series forecasting
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Summary:A new method for identification of an optimal set of time lags based on non-uniform attractor embedding from the observed non-linear time series is proposed in this paper. Simple deterministic method for the determination of non-uniform time lags comprises the pre-processing stage of the time series forecasting algorithm which is implemented in the form of a fuzzy inference system. Identification of embedding parameters of the underlying dynamical system includes not only optimization of time lags but also determination of optimal dimension of the reconstructed phase space. Experiments done with benchmark chaotic time series show that the proposed method can considerably improve the forecasting accuracy. The proposed method seems to be an efficient candidate for prediction of time series with multiple time scales and noise.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2008.10.010