Effective lengths of intervals to improve forecasting in fuzzy time series

Abstract

Length of intervals affects forecasting results in fuzzy time series. Unfortunately, the issue of how to determine effective lengths of intervals has not been touched in previous studies. This study proposes distribution- and average-based length to approach this issue. Distribution-based length is the largest length smaller than at least half the first differences of data. Average-based length is set to one half the average of the first differences of data. Empirical analyses show that distribution- and average-based lengths are simple to calculate and can greatly improve forecasting results; in particular, they are superior to the randomly chosen lengths used in previous studies. © 2001 Elsevier Science B.V. All rights reserved.