This study uses two data sets, Taiwanese export values as the prediction varialbe and its foreign exchange spot rates as the auxiliary variable, to discuss two forecasting issues in the fuzzy time series analysis by using One-and Two-factor models. First, disregarding the length of information period, when window basis is two, a better forecasting result is obtained. Second, the optimum number of partition equal intervals is to select 14 intervals to have the smallest MSE in all models. However, if partitioning the information into more than 14 equal intervals, MSE can not be reduced but presents a waving pattern.
Fuzzy time series methods have been applied to social forecasting for over a decade; however, little research has been done to discuss the decision of an optimal fuzzy model for time series. In the paper, we evaluate the forecasting performance of three listed multivariate fuzzy models by comparing forecasting MSE of model. The data obtained from AEROM, Taiwan, includes Taiwan’s exports and foreign exchange rate for models’ test. The algorithm for predictive value of the models has three-stage computation procedure: First, calibrating time series correlation, deciding window base and interval partition; second, solving the static forecasting value of each model; third, comparing the dynamic parameter to impact of the forecasting error. The empirical results indicate that increasing predictor variables has no significant effect on predictive performance of the models; increasing length of interval would not improve the prediction performance of the models. Moreover, Fuzzy model is better for short-term time series forecasting. For forecasting purpose, Heuristic model has best forecasting performance among three fuzzy models. The findings of the paper represent a significant contribution to our understanding of the applicability of fuzzy models to predict.
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