Time Series Prediction Model of Grey Wolf Optimized Echo State Network

Authors

Huiqing Wang College of Information and Computer, Taiyuan, University of Technology, Taiyuan, Shanxi
Yingying Bai College of Information and Computer, Taiyuan, University of Technology, Taiyuan, Shanxi
Chun Li College of Information and Computer, Taiyuan, University of Technology, Taiyuan, Shanxi, https://orcid.org/0000-0003-2311-3422
Zhirong Guo College of Information and Computer, Taiyuan, University of Technology, Taiyuan, Shanxi
Jianhui Zhang Postal Savings Bank of China

DOI:

https://doi.org/10.5334/dsj-2019-016

Keywords:

Echo State Network, Grey Wolf optimization, time series prediction

Abstract

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^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.

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Published

2019-05-07

Issue

Vol. 18 (2019)

Section

Research Papers

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