A dynamic approach to adjusting lengths of intervals in fuzzy time series forecasting

Abstract

Fuzzy time series models have been proposed to model linguistic observations and have been extended to model numerical observations as well. Many factors are believed to affect fuzzy time series forecasting. The formulation of fuzzy relationships and the lengths of intervals for observations are considered two of them. Hence, how to cover both issues simultaneously is important for the improvement of forecasting results. This study proposes a dynamic approach to adjusting lengths of intervals in fuzzy time series forecasting, thus capturing fuzzy relationships more appropriately. These fuzzy relationships can then be used to improve forecasting. Enrollment and stock index forecasting are used to demonstrate the effectiveness of the dynamic approach. Empirical results show that this dynamic approach can be applied to improve fuzzy time series forecasting.