A new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques

Abstract

This paper presents a new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. We fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors high-order fuzzy logical relationships. Then, we group the two-factors high-order fuzzy logical relationships into two-factors high-order fuzzy-trend logical relationship groups. Finally, we obtain the optimal weighting vectors for each fuzzy-trend logical relationship group by using particle swarm optimization techniques to perform the forecasting. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.