An efficient forecasting model based on an improved fuzzy time series and a modified group search optimizer

This paper presents a prediction model based on an improved fuzzy time series (IFTS) and a modified group search optimizer to effectively solve forecasting problems. IFTS can accurately predict whether subsequent predicted data will increase or decrease according to ratio value in the fuzzy logical relationship. In addition, the modified group search optimizer is used to adjust the length of an interval. The proposed prediction model is also used to forecast the enrollments of the University of Alabama the enrollments of a university of technology in central Taiwan, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) Experimental results show that the proposed model obtains the smallest prediction error than those of other methods.