Handling forecasting problems based on fuzzy time series model and model error learning

Abstract

Classical fuzzy time series forecasts are comprised of three steps: fuzzification, identification of fuzzy relation, and defuzzification. In this paper, we propose a new approach and add an error learning step to improve forecasts. In the fuzzification step, a hybrid method, based on the fuzzy c-means clustering and the fuzzy Silhouette criterion, is employed to determine the optimal number of intervals, which avoids time-consuming iterations of the whole algorithm. In the defuzzification step, an optimization model is set up to explain the rule of defuzzification. In the model structure, an error term is assembled into the traditional model to express model error, which is predicted by linear fitting and abnormal errors processing. Learning of model errors and considering of data characteristics guarantee good interpretability and accuracy. The numerical results show that the proposed approach has superior forecast performance to existing methods.