TITLE:
A Prediction Method Based on Improved Echo State Network for COVID-19 Nonlinear Time Series
AUTHORS:
Banteng Liu, Wei Chen, Yourong Chen, Ping Sun, Heli Jin, Hao Chen
KEYWORDS:
COVID-19, Nonlinear Time Series, Prediction, Echo State Network
JOURNAL NAME:
Journal of Computer and Communications,
Vol.8 No.12,
December
24,
2020
ABSTRACT: This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservoir topology and the output weight matrix, and adopt the ABC (Artificial Bee Colony) algorithm based on crossover and crowding strategy to optimize the parameters. Finally, the proposed method is simulated and the results show that it has stronger prediction ability for COVID-19 nonlinear time series.