A Novel Fuzzy Time Series Forecasting Model Based on the Hybrid Wolf Pack Algorithm and Ordered Weighted Averaging Aggregation Operator

Abstract

The fuzzy time series has received extensive attention since it was proposed and it has been widely used in various practical applications. This study proposes a new fuzzy time series forecasting model which considers a hybrid wolf pack algorithm (HWPA) and an ordered weighted averaging (OWA) aggregation operator for fuzzy time series. The HWPA is adopted to obtain a suitable partition of the universe of discourse to promote the forecasting performance. Furthermore, the improved OWA aggregation method is applied to make the aggregation of historical information more practical. To overcome the deficiency of slow convergence speed and easy to entrap into the local extremum of the wolf pack algorithm (WPA), the chemotactic behavior and elimination–dispersal behavior of bacterial foraging optimization (BFO) are employed to optimize the scouting behavior of WPA. The actual enrollments data of the University of Alabama and Taiwan Futures Exchange (TAIFEX) are utilized as the benchmark data and the computational results of both training and testing phases all indicate that the new forecasting model outperforms other existing models. The robustness of the proposed model is also tested and the robust results can be obtained when the historical data are inaccurate.