A Hybrid Method for Forecasting of Fuzzy Time Series

Fuzzy time series approaches have been used for real world time series contain uncertainty. When these approaches are used, it is not necessary to satisfy the assumptions needed for conventional time series methods. Fuzzy time series methods are composed of three phases which are fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are widely employed in these phases. Genetic algorithm and differential evolution algorithm are one of the most popular artificial intelligence algorithms. Besides, the hybrid algorithms by obtaining the composed of some artificial intelligence algorithms have been frequently used in the literature. In this paper, a hybrid method composed by genetic algorithm and differential evolution algorithms is proposed to find the optimal interval lengths. The hybrid method proposed in this paper has been applied to Canadian lynx data and its superior forecasting performance was shown when compared with those obtained by other techniques suggested in the literature.

Keywords: Differential evolution algorithm, Forecasting, Fuzzy time series, Genetic algorithm, Hybrid method, Mutation operator.