Recently, numerous scholars have suggested fuzzy time series (FTS) models to forecast many different fields. One of the vital issues for high accurate forecasting in FTS model is method of partitioning in Universe of discourse (UoD). In this research, we propose a novel FTS model, which is established by using hedge algebra (HA) and particle swarm optimization (PSO) for forecasting the different problems. In this model, HA is considered an algebraic structure for partitioning the UoD into unequal-size intervals based on optimal parameters which is determined by PSO. After making the intervals with unequallength, the data values of times series on each interval are symbolized by fuzzy sets and then, these fuzzy sets are utilized to make fuzzy relation groups. Lastly, we keep using the PSO to adjust the size of each interval with view to reaching the better accurate prediction rate. The effectiveness of the proposed method is demonstrated on two datasets which are often applied in many studies in literature as enrolments data of the University of Alabama and Car road accident data in Belgium. The obtained results show that the proposed model produces higher accuracy forecasting when compared with the some of the recent FTS prediction models for all orders of model.
In recent years, numerous fuzzy time series (FTS) forecasting models have been widely used. One of the important factors for obtaining high forecasting accuracy in fuzzy time series model is that the lengths of intervals in the universe of discourse. In this study, a hybrid forecasting model which uses hedge algebra (HA) and particle swarm optimization (PSO) is proposed to determine optimal lengths of intervals in FTS models. In that, HA is utilized as a tool to partition the universe of discourse into intervals with unequal-size corresponding to the semantic intervals calculated from the linguistic terms. After processing of generating the intervals, we define fuzzy sets based on the observation data of times series and use them to establish fuzzy relationship groups. Then, the proposed model is combined with the PSO technique to find the appropriate length of each interval with view to reaching the better forecasting accuracy rate. The performance of the proposed model is evaluated with the historical data of enrolments at the University of Alabama. The simulated results obtained indicate that the proposed model achieves higher forecasting accuracy compared other existing forecasting models and it can obtain better quality solutions for both the 1st-order and high-order FTS model. HIGHLIGHTS In fuzzy time series forecasting model, the length of intervals and the order of fuzzy relationships are two critical factors for forecasting accuracy Hedge algebra and PSO are utilized as a tool to partition the universe of discourse into intervals with unequal - size corresponding to the semantic intervals calculated from the linguistic terms The defuzzification principles are used to calculate the forecasting results based on the fuzzy relationship groups GRAPHICAL ABSTRACT
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