Fractional stochastic configuration networks-based nonstationary time series prediction and confidence interval estimation

作者：

Highlights：

• Stochastic configuration network is applied for nonstationary time series prediction.

• Fractional difference is introduced to improve the stochastic configuration network.

• The confidence interval is calculated directly with the prediction uncertainty.

• The proposed methodology is verified on a real supermarket cooling system.

摘要

•Stochastic configuration network is applied for nonstationary time series prediction.•Fractional difference is introduced to improve the stochastic configuration network.•The confidence interval is calculated directly with the prediction uncertainty.•The proposed methodology is verified on a real supermarket cooling system.

论文关键词：Nonstationary time series,Stochastic configuration networks,Fractional order differential,Hurst exponent,Time series regression,Confidence interval estimation

论文评审过程：Received 9 March 2020, Revised 29 November 2021, Accepted 29 November 2021, Available online 20 December 2021, Version of Record 29 December 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116357