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.
Highlights
The time series are a group of random variables arranged in time order and has been widely applied in our daily life and industry, including commerce, meteorology, finance, and agriculture
In order to better verify the performance of the time series prediction model, this experiment selected seven sets of data, of which the first five groups are nonlinear data., including the EEG public EEG data EEG, China Statistical Yearbook official website 1999–2008 different influencing factors The Shanghai Railway Index in the historical stock index data of the railway passenger traffic volume, China’s 1985–2011 grain production data 1, 2 and Netease Financial Network 1990/12/20—1991/1/24
The BP neural network model (Zhai and Cao 2016), the Elman neural network model (Liang et al 2017), and the Echo State Networks (ESN) model (Li et al 2012), ESN prediction model based on recursive least squares (Chouikhi et al 2017), PSO optimization based ESN model (Zhang et al 2015), and the proposed GWO_ESN model were involved in this prediction experiment
Summary
The time series are a group of random variables arranged in time order and has been widely applied in our daily life and industry, including commerce, meteorology, finance, and agriculture. The network training process uses linear regression and has short-term memory function and the network model is simple and fast, has high prediction performance, overcomes the problems of large computational complexity, low training efficiency and local optimization in traditional recurrent neural networks, and can be adapted to the processing of time series data in practical problems. Qin et al proposed a novel E-KFM model by combining the KFM algorithm and ESN and applied it for multistep prediction of time sequence data (Xiao et al 2017). The genetic algorithm has complex coding, many parameters and choices rely on experience, which cannot solve the problem of large-scale calculation Aimed at this issue, a time series prediction model of Grey Wolf optimized ESN is proposed by introducing the Grey Wolf algorithm, a swarm intelligence optimization algorithm. The proposed model is verified based on different data sets
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