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

ALADAĞ Ç. H., Basaran M. A., Egrioglu E., Yolcu U., Uslu V. R.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.36, sa.3, ss.4228-4231, 2009 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 3
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1016/j.eswa.2008.04.001
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.4228-4231
  • Anahtar Kelimeler: Forecasting, Fuzzy relation, Fuzzy set, High order fuzzy time series, Neural networks, ENROLLMENTS
  • Marmara Üniversitesi Adresli: Hayır

Özet

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.