Forecasting nonlinear time series with a hybrid methodology

ALADAĞ Ç. H., EĞRİOĞLU E., KADILAR C.

APPLIED MATHEMATICS LETTERS, cilt.22, sa.9, ss.1467-1470, 2009 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 9
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1016/j.aml.2009.02.006
  • Dergi Adı: APPLIED MATHEMATICS LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1467-1470
  • Hacettepe Üniversitesi Adresli: Evet

Özet

In recent years, artificial neural networks (ANNs) have been used for forecasting in time series in the literature. Although it is possible to model both linear and nonlinear structures in time series by using ANNs, they are not able to handle both structures equally well. Therefore, the hybrid methodology combining ARIMA and ANN models have been used in the literature. In this study, a new hybrid approach combining Elman's Recurrent Neural Networks (ERNN) and ARIMA models is proposed. The proposed hybrid approach is applied to Canadian Lynx data and it is found that the proposed approach has the best forecasting accuracy. (c) 2009 Elsevier Ltd, All rights reserved.