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Time series prediction based on echo state network tuned by divided adaptive multi-objective differential evolution algorithm
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DOI：10.1007/s00500-020-05457-8.png)
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For echo state networks, it is difficult to select suitable reservoir parameters for different applications. In this paper, we put forward a divided adaptive multi-objective differential evolution (DAMODE) algorithm to optimize the reservoir parameters of echo state network. To improve the performance of multi-objective differential evolution algorithm, the entire population is divided into several subpopulations, and each subpopulation is divided into two subsets to compromise convergence and diversity, which are updated according to certain rules. Besides, the scale factor and crossover rate of differential evolutionary algorithm are adaptively adjusted. Experiments were conducted on the Lorenz time series, hourly temperature time series and PM2.5 time series in Beijing. Experiment results show that the proposed model can improve prediction accuracy and has good generalization ability and practicability.
Keyword:
Time series
Prediction
Multi-objective differential evolution
Echo state network
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2.5
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