Abstract

Elham Kahforoushan, Masoumeh Zarif and Ebrahim Badali Mashahir

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.