Time Series Prediction Model of Grey Wolf Optimized Echo State Network

As a novel recursion neural network, Echo State Networks (ESN) are characterized by strong nonlinear prediction capability and effective and straightforward training algorithms. However, conventional ESN predictions require a large volume of training samples. Meanwhile, the time sequence data are co...

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Bibliographic Details
Published inData science journal Vol. 18; no. 1
Main Authors Wang, Huiqing, Bai, Yingying, Li, Chun, Guo, Zhirong, Zhang, Jianhui
Format Journal Article
LanguageEnglish
Published Ubiquity Press Ltd 07.05.2019
Ubiquity Press
Subjects
Algorithms
Artificial neural networks
Echo State Network
Grey Wolf optimization
Mathematical research
Novels
Optimization theory
Time series analysis
time series prediction
China
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Summary:As a novel recursion neural network, Echo State Networks (ESN) are characterized by strong nonlinear prediction capability and effective and straightforward training algorithms. However, conventional ESN predictions require a large volume of training samples. Meanwhile, the time sequence data are complicated and unstable, resulting in insufficient learning of this network and difficult training. As a result, the accuracies of conventional ESN predictions are limited. Aimed at this issue, a time series prediction model of Grey Wolf optimized ESN has been proposed. W[sup.out] of ESN was optimized using the Grey Wolf algorithm and predictions of time series data were achieved using simplified training. The results indicated that the optimized time series prediction method exhibits superior prediction accuracy at a small sample size, compared with conventional prediction methods.
ISSN:1683-1470
1683-1470
DOI:10.5334/dsj-2019-016