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. Wout 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.