Chaotic Time Series Prediction Based on Evolving Recurrent Neural Networks

• Published in: 2007 International Conference on Machine Learning and Cybernetics Vol. 6; pp. 3496 - 3500
• Main Authors: Qian-Li Ma, Qi-Lun Zheng, Hong Peng, Tan-Wei Zhong, Li-Qiang Xu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2007
• 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
• ISBN: 1424409721; 9781424409723
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2007.4370752

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.