A new model selection strategy in artificial neural networks

Eǧrioǧlu E., ALADAĞ Ç. H., Günay S.

Applied Mathematics and Computation, cilt.195, sa.2, ss.591-597, 2008 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 195 Sayı: 2
  • Basım Tarihi: 2008
  • Doi Numarası: 10.1016/j.amc.2007.05.005
  • Dergi Adı: Applied Mathematics and Computation
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.591-597
  • Anahtar Kelimeler: Artificial neural networks, Feed forward neural networks, Model selection criteria, Time series forecasting
  • Hacettepe Üniversitesi Adresli: Evet

Özet

In recent years, artificial neural networks have been used for time series forecasting. Determining architecture of artificial neural networks is very important problem in the applications. In this study, the problem in which time series are forecasted by feed forward neural networks is examined. Various model selection criteria have been used for the determining architecture. In addition, a new model selection strategy based on well-known model selection criteria is proposed. Proposed strategy is applied to real and simulated time series. Moreover, a new direction accuracy criterion called modified direction accuracy criterion is discussed. The new model selection strategy is more reliable than known model selection criteria. © 2007 Elsevier Inc. All rights reserved.