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Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations

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  • Cagdas H. Aladag
  • Murat A. Basaran
  • Erol Egrioglu
  • Ufuk Yolcu
  • Vedide R. Uslu
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<mark>Journal publication date</mark>1/01/2009
<mark>Journal</mark>Expert Systems with Applications
Issue number3 PART 1
Volume36
Number of pages4
Pages (from-to)4228-4231
Publication StatusPublished
<mark>Original language</mark>English

Abstract

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.