Type-1 recurrent intuitionistic fuzzy functions for forecasting

作者：

Highlights：

• A forecasting method based on T1FFs that employs ARMA, GWO and IFCM is introduced.

• Hesitancy of an object belonging to a cluster is considered for more accurate results.

• GWO is employed to obtain more accurate results and faster computation time.

• T1-R-IFFs give very competitive forecasting results with better calculation times.

摘要

•A forecasting method based on T1FFs that employs ARMA, GWO and IFCM is introduced.•Hesitancy of an object belonging to a cluster is considered for more accurate results.•GWO is employed to obtain more accurate results and faster computation time.•T1-R-IFFs give very competitive forecasting results with better calculation times.

论文关键词：Forecasting,Grey wolf optimizer,Type-1 fuzzy functions,Intuitionistic fuzzy sets,Non-linear forecasting

论文评审过程：Received 25 July 2018, Revised 2 August 2019, Accepted 31 August 2019, Available online 2 September 2019, Version of Record 8 September 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.112913