A refined fuzzy time-series model for forecasting

Fuzzy time-series models have been used to model observations, where each one of them contains multiple values. The formulation of fuzzy relationships and the lengths of intervals are considered to be two of the critical factors that affect forecasting results. Unfortunately, the lengths of the intervals were determined during the early stages of forecasting in these models, and they thus often failed to reflect the distribution of observations. This study therefore proposes a refined fuzzy time-series model to further refine the lengths of intervals. This model can refine the lengths of intervals during the formulation of fuzzy relationships, and hence capture the fuzzy relationships more appropriately. As a result, the forecasting results can be improved. Both the stock index and enrollment are used as the targets in the empirical analysis.