Time series forecasting by the novel Gaussian process wavelet self-join adjacent-feedback loop reservoir model

作者：

Highlights：

• This study develops a novel GP-SALR model to improve execution efficiency.

• The GP-WSALR adopts Gaussian process regression and wavelet neurons mechanisms.

• The GP-WSALR can obtain superior performance in time series forecasting problems.

摘要

•This study develops a novel GP-SALR model to improve execution efficiency.•The GP-WSALR adopts Gaussian process regression and wavelet neurons mechanisms.•The GP-WSALR can obtain superior performance in time series forecasting problems.

论文关键词：Time series forecasting,Gaussian process regression,Echo state network,Electrical load forecasting,Network traffic forecasting

论文评审过程：Received 20 September 2021, Revised 12 January 2022, Accepted 25 February 2022, Available online 7 March 2022, Version of Record 11 March 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116772