A hybrid pso-sa scheme for improving accuracy of fuzzy time series forecasting models

Abstract

Forecasting methods based on fuzzy time series have been examined intensively during the last few years. Three main factors which affect the accuracy of those forecasting methods are the length of intervals, the way of establishing fuzzy logical relationship groups, and defuzzification techniques. Many researchers focus on studying the methods of optimizing the length of intervals to improve forecasting accuracies by utilizing various optimization techniques. In line with that re- search trend, this paper proposes a hybrid algorithm combining particle swarm optimization with the simulated annealing technique (PSO-SA) to optimize the length of intervals to improve forecasting accuracies. The experimental results on the datasets of the "enrolments of the University of Al-abama," "killed in car road accidents in Belgium," and the "spot gold in Turkey" have shown that the proposed forecasting model is more effective than their counterparts.