In the literature, fuzzy time series forecasting models generally include fuzzy lagged variables. Thus, these fuzzy time series models have only autoregressive structure. Using such fuzzy time series models can cause modeling error and bad forecasting performance like in conventional time series analysis. To overcome these problems, a new first-order fuzzy time series which forecasting approach including both autoregressive and moving average structures is proposed in this study. Also, the proposed model is a time invariant model and based on particle swarm optimization heuristic. To show the applicability of the proposed approach, some methods were applied to five time series which were also forecasted using the proposed method. Then, the obtained results were compared to those obtained from other methods available in the literature. It was observed that the most accurate forecast was obtained when the proposed approach was employed.
Improved Fuzzy Time Series Forecasting Model Based on Optimal Lengths of Intervals Using Hedge Algebras and Particle Swarm Optimization
