Journal of
Development and Agricultural Economics

  • Abbreviation: J. Dev. Agric. Econ.
  • Language: English
  • ISSN: 2006-9774
  • DOI: 10.5897/JDAE
  • Start Year: 2009
  • Published Articles: 546

Full Length Research Paper

Prediction of added value of agricultural subsections using artificial neural networks: Box-Jenkins and Holt-Winters methods

Elham Kahforoushan1*, Masoumeh Zarif1 and Ebrahim Badali Mashahir2
1Department of Agricultural Economics, Faculty of Agricultural Engineering, University of Zabol, Zabol, Iran. 2Tarh Ab Araz Consulting Engineers Company, Iran.
Email: [email protected]

  •  Accepted: 11 January 2010
  •  Published: 30 April 2010

Abstract

Added value of agricultural sub sectors is affected by many factors such as quantity production per agricultural sub sectors and selling price of producers and is related to some factors such as government investment and monetary and financial policies. This study examines the performance of artificial neural network, Box-Jenkins and Holt-Winters-no-seasonal models in forecasting added value of agricultural sub sectors in Iran. It compares error criterions for determining the best model. Results showed that Box-Jenkins and artificial neural network are appropriate and artificial neural network indicated good result relatively in learn stage, but Box-Jenkins model gave better results in forecasting of unseen data. Holt-Winters model had the lowest mean absolute percent error in both of model fitting and model validation stages.

 

Key words: Artificial neural network, Box-Jenkins, Holt-Winters, added value of agricultural sub sectors.