A NEW ARCHITECTURE SELECTION STRATEGY IN SOLVING SEASONAL AUTOREGRESSIVE TIME SERIES BY ARTIFICIAL NEURAL NETWORKS
HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, cilt.37, sa.2, ss.185-200, 2008 (SCI-Expanded, Scopus, TRDizin)
- 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.