Chaotic Time Series Prediction Based on Evolving Recurrent Neural Networks

The prediction of future values of a time series generated by a chaotic dynamical system is a challenging task. Recently, the use of recurrent neural networks (RNN) models appears. An evolving neural network (ERNN) is proposed for the prediction of chaotic time series, which estimates the proper par...

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Bibliographic Details
Main Authors: Qian-Li Ma, Qi-Lun Zheng, Hong Peng, Tan-Wei Zhong, Li-Qiang Xu
Format: Conference Proceeding
Language:English
Subjects:
Chaos
Chaotic time series
Delay effects
Evolutionary algorithms
Evolutionary computation
Machine learning
Neural networks
Neurons
Parameter estimation
Phase estimation
Prediction
Predictive models
Recurrent neural networks
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Description
Summary:The prediction of future values of a time series generated by a chaotic dynamical system is a challenging task. Recently, the use of recurrent neural networks (RNN) models appears. An evolving neural network (ERNN) is proposed for the prediction of chaotic time series, which estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent neural networks by evolutionary algorithms. The effectiveness of ERNN is evaluated by using four benchmark chaotic time series data sets: Lorenz series, logistic series, Mackey-Glass series and real-world sun spots series. Our experiments indicate that the prediction performances of ERNN are better than the other methods exiting in the bibliography.
ISSN:2160-133X
DOI:10.1109/ICMLC.2007.4370752