An interval type-2 fuzzy regression model with crisp inputs and type-2 fuzzy outputs for TAIEX forecasting

摘要

The type-2 fuzzy logic system permits us to model uncertainties existing in membership functions. Accordingly, this study aims to propose a linear framework for an interval type-2 fuzzy regression model based on the existing possibilistic models. In this model, vagueness is minimized, under the circumstances where the h-cut of observed value is included in predicted value. In this model both primary and secondary membership function of predicted value fit the observed value. This model, without the additional complexities, demonstrates its ability compared to previous type-2 fuzzy models. The Taiwan stock index forecasting is used to evaluate model efficiency.