TIME SERIES FORECASTING USING A HYBRID NEURAL NETWORKS AND NONPARAMETRIC REGRESSION MODEL

AYDIN D., Mammadov M.

PAKISTAN JOURNAL OF STATISTICS, cilt.30, sa.3, ss.319-332, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 30 Sayı: 3
  • Basım Tarihi: 2014
  • Dergi Adı: PAKISTAN JOURNAL OF STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.319-332
  • Anahtar Kelimeler: Hybrid models, Multilayer perceptrons, Radial basis function, Regression spline, Smoothing spline, ARIMA
  • Anadolu Üniversitesi Adresli: Evet

Özet

The focus in this paper embraces the hybrid models whose components are nonparametric regression and artificial neural networks. Smoothing spline, regression spline and additive regression models are considered as the nonparametric regression components. Furthermore, various multilayer perceptron algorithms and radial basis function network model are regarded as the artificial neural networks components. The performances of these models are compared by forecasting three real Turkish data sets: Domestic product per capita (GDP), the number of cars produced and the number of tourist arrivals. The results obtained by experimental evaluations show that hybrid models proposed in this paper have performed much better in comparison to hybrid models discussed in literature.