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, vol.37, no.2, pp.185-200, 2008 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 2
  • Publication Date: 2008
  • Journal Name: HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.185-200
  • Hacettepe University Affiliated: Yes

Abstract

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.