In the literature, there have been many studies using fuzzy time series for the purpose of forecasting. The
most studied model is the first order fuzzy time series model. In this model, an observation of fuzzy time
series is obtained by using the previous observation. In other words, only the first lagged variable is used
when constructing the first order fuzzy time series model. Therefore, this model can not be sufficient for
some time series such as seasonal time series which is an important class in time series models. Besides,
the time series encountered in real life have not only autoregressive (AR) structure but also moving average
(MA) structure. The fuzzy time series models available in the literature are AR structured and are not
appropriate for MA structured time series. In this paper, a hybrid approach is proposed in order to analyze
seasonal fuzzy time series. The proposed hybrid approach is based on partial high order bivariate fuzzy
time series forecasting model which is first introduced in this paper. The order of this model is determined
by utilizing Box-Jenkins method. In order to show the efficiency of the proposed hybrid method,
real time series are analyzed with this method. The results obtained from the proposed method are compared
with the other methods. As a result, it is observed that more accurate results are obtained from the
proposed hybrid method.