This paper presents an efficient and simplified type-1 and interval type-2
non singleton fuzzy logic systems (NSFLSs) in order to obviate time series forecasting
problems. These methods have applied non singleton fuzzification by Sharp Gaussian
membership function, logical inference with the First-Infer-Then-Aggregate (FITA)
approach and parametric defuzzification. Rules are generated based on high order fuzzy
time series. In interval type-2 FLS, which can better handle uncertainties, type-2 sets
are generated, using fuzzy normal forms by applying Yager Parametric classes of
operators. Moreover, in these systems, some elements such as membership functions,
operators and length of intervals affect the forecasting results. In addition, a method for
tuning parameters of fuzzy logic systems with genetic algorithm is presented. Finally,
the proposed methods are applied to predict the temperature and the Taiwan Stock
Exchange (TAIEX). The results show the higher degree of accuracy of the model
compared to the previous methods.
Keywords: Forecasting, Fuzzy Time Series, Genetic Algorithm, Interval Type-2.
