A NEW ARCHITECTURE SELECTION STRATEGY IN SOLVING SEASONAL AUTOREGRESSIVE TIME SERIES BY ARTIFICIAL NEURAL NETWORKS

ALADAĞ Ç. H., EĞRİOĞLU E., GÜNAY S.

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, cilt.37, sa.2, ss.185-200, 2008 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Sayı: 2
  • Basım Tarihi: 2008
  • Dergi Adı: HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.185-200
  • Hacettepe Üniversitesi Adresli: Evet

Özet

The only suggestions given in the literature for determining the architecture of neural networks are based on observations, and a simulation study to determine the architecture has not yet been reported. Based on the results of the simulation study described in this paper, a new architecture selection strategy is proposed and shown to work well. It is noted that although in some studies the period of a seasonal time series has been taken as the number of inputs of the neural network model, it is found in this study that the period of a seasonal time series is not a parameter in determining the number of inputs.