Random forests-based extreme learning machine ensemble for multi-regime time series prediction

作者：

Highlights：

• The random forests-based extreme learning machine ensemble model is proposed.

• The model can be accurate, stable and efficient in time series prediction.

• The multi-regime approach is used to handle multi-regime time series.

• The model and the approach are combined to predict multi-regime time series.

摘要

•The random forests-based extreme learning machine ensemble model is proposed.•The model can be accurate, stable and efficient in time series prediction.•The multi-regime approach is used to handle multi-regime time series.•The model and the approach are combined to predict multi-regime time series.

论文关键词：Extreme learning machine,Random forests,Ensemble learning,Multi-regime time series,Time series prediction

论文评审过程：Received 10 January 2016, Revised 3 April 2017, Accepted 4 April 2017, Available online 5 April 2017, Version of Record 28 April 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.04.013