Fuzzy Time Series Model Based on Fitting Function for Forecasting TAIEX Index

Many traditional time series model has been widely applied in forecasting Problem. However, the previous time series methods still have some constraints: (1) conventional time series models only considered single variable; (2) traditional fuzzy time series model determined the interval length of linguistic value subjectively; (3) selecting variables depended on personal experience and opinion. Hence, this paper proposes a novel hybrid fuzzy time series model based on fitting function to forecast TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock index). The proposed model employed Pearson’s correlation to select important technical indicators objectively, and the proposed model utilized fitting function to forecast TAIEX Index. In verification, the collected TAIEX datasets from 1998/01/03 to 2002/12/31 are used as experimental dataset and the root mean square error (RMSE) as evaluation criterion. The results show that the proposed model outperforms the listing models in accuracy.