Determining Interval Length in Fuzzy Time Series by Using an Entropy Based Approach
Pp. 78-87 (10)
Cagdas Hakan Aladag, Irem Degirmenci and Suleyman Gunay
Various theoretical assumptions in conventional time series methods do not need to be
checked in fuzzy time series approach. Therefore fuzzy time series are preferred in many applications.
The identification of the length of intervals is an important issue and affects the forecasting
performance. But in many studies in the literature, the length of intervals is determined randomly.
Starting from this point, Huarng  has proposed two novel approaches which are based on the
distribution and the average to choose a more effective length. Huarng and Yu  used a dynamic
approach for adjusting lengths of interval. Huarng  suggested a different method which is called ratio
based lengths of intervals. Cheng et al.  have proposed a new approach by using entropy. Eǧrioǧlu et
al.  and Yolcu et al.  have determined the lengths of intervals by using optimization. At the first
stage of the method proposed by Cheng et al. , a specific method has not been used and classes have
been assigned intuitively while classes to which data belong were generating. In this study, the
approach proposed by Degirmenci et al.  is applied to the enrollment data at the University of
Alabama and the yearly data of the quantities of clean water used in Istanbul. Then obtained forecasts
are compared with those obtained from other methods available in the literature.
Entropy, Forecasting, Length of interval, Fuzzy c-means clustering, Fuzzy time series.
Hacettepe University, Faculty of Science, Department of Statistics, 06800, Ankara, Turkey