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    A new approach for determining the length of intervals for fuzzy time series
    (ELSEVIER SCIENCE BV, 2009) Yolcu, Ufuk; Egrioglu, Erol; Uslu, Vedide R.; Basaran, Murat A.; Aladag, Cagdas H.
    In the implementations of fuzzy time series forecasting, the identification of interval lengths has an important impact on the performance of the procedure. However, the interval length has been chosen arbitrarily in many papers. Huarng developed a new approach which is called ratio-based lengths of intervals in order to identify the length of intervals. In our paper, we propose a new approach which uses a single-variable constrained optimization to determine the ratio for the length of intervals. The proposed approach is applied to the two well-known time series, which are enrollment data at The University of Alabama and inventory demand data. The obtained results are compared to those of other methods. The proposed method produces more accurate predictions for the future values of used time series. (c) 2008 Elsevier B.V. All rights reserved.
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    Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations
    (PERGAMON-ELSEVIER SCIENCE LTD, 2009) Aladag, Cagdas H.; Basaran, Murat A.; Egrioglu, Erol; Yolcu, Ufuk; Uslu, Vedide R.
    A given observation in time series does not only depend on preceding one but also previous ones in general. Therefore, high order fuzzy time series approach might obtain better forecasts than does first order fuzzy time series approach. Defining fuzzy relation in high order fuzzy time series approach are more complicated than that in first order fuzzy time series approach. A new proposed approach, which uses feed forward neural networks to define fuzzy relation in high order fuzzy time series, is introduced in this paper. The new proposed approach is applied to well-known enrollment data for the University of Alabama and obtained results are compared with other methods proposed in the literature. It is found that the proposed method produces better forecasts than the other methods. (c) 2008 Elsevier Ltd. All rights reserved.