Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting

HAMZAÇEBİ C., Akay D., Kutay F.

EXPERT SYSTEMS WITH APPLICATIONS, vol.36, no.2, pp.3839-3844, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 2
  • Publication Date: 2009
  • Doi Number: 10.1016/j.eswa.2008.02.042
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.3839-3844
  • Karadeniz Technical University Affiliated: No


Artificial neural network is a valuable tool for time series forecasting. In the case of performing multiperiodic forecasting with artificial neural networks. two methods, namely iterative and direct, can be used. In iterative method, first subsequent period information is predicted through past observations. Afterwards, the estimated value is used as an input; thereby the next period is predicted. The process is carried on until the end of the forecast horizon. In the direct forecast method, successive periods can be predicted all at once. Hence, this method is thought to yield better results as only observed data is utilized in order to predict future periods. In this study, forecasting was performed using direct and iterative methods, and results of the methods are compared using grey relational analysis to find the method which gives a better result. (C) 2008 Elsevier Ltd. All rights reserved.