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

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.