The Effect of the Length of Interval in Fuzzy Time Series Models on Forecasting
Pp. 64-77 (14)
Erol Eǧrioǧlu and Cagdas Hakan Aladag
Due to the vagueness that they contain in their observations, fuzzy time series models
worked in two main categories such as first order and high order models, has an ever expending field of
study. Fuzzy time series analysis method is highly effective in uncovering the relations of this type of
time series structure. In the implementation of fuzzy time series methods, it is crucial to determine the
model order in terms of forecasting performance. Besides, regardless of the model order, the length of
interval determined in the partition phase of the universe of discourse, greatly affects forecasting
performance. Therefore, there have been numerous studies focusing on determining the length of
interval in the literature. This study aims to introduce the significance of interval length determination
in fuzzy time series analysis method on forecasting performance. For this purpose, related methods are
introduced, implementation of two real time series is shown and some comparisons between methods
are made and finally obtained results are discussed.
Fuzzy time series, Forecasting, Length of interval, Optimization.
Ondokuz Mayis University, Faculty of Arts and Science, Department of Statistics, 55139, Samsun, Turkey