An improved fuzzy forecasting method for seasonal time series

Several time-variant fuzzy time series models have been developed during the last decade. These models usually focus on forecasting stationary of trend time series, but they are not suitable for forecasting seasonal time series. Furthermore, several factors that affect the forecasting accuracy are not carefully examined, such as interval length, interval number, and level of window base. Aiming to solve these issues, the goal of this study is to develop an improved fuzzy time series forecasting method that can effectively deal with seasonal time series. The proposed method can determine appropriate length interval. Moreover, a systematic search algorithm is used to find the best window base. The proposed method can provide decision analysts with more precise forecasted values. Two numerical data sets are employed to illustrate the proposed method and to compare the forecasting accuracy between the proposed method and four fuzzy time series methods. The results of the comparison indicate that the proposed method produces more accurate forecasted results.