Application of Artificial Neural Network Model in Predicting Price of Milk in Iran

Abstract

Changing economic welfare is one of the most important parameters considered by politicians in applying economic policies in agricultural sector. Modifying expenditures is a factor that influences on producers and consumers` economic welfare. Due to the significant impact it has on nutrition and food, job and income of society, milk is a product that is supported by Iranian government. Objective of the research is to predict price of farm gate milk by applying ARIMA and Artificial Neural Networks (ANN). Data from February 2006 to March 2013 were collected from Bureau of Animal Husbandry and Agriculture Support of Iran. The data used had the ability of prediction. Econometric criteria such as , MAD, MAPE and RMSE were also used in order to compare ARIMA error prediction. The results indicate that ANN demonstrated minor error for predicting milk price in a five-month time horizon and it is more accurate than the ARIMA method. Both models predict high fluctuations in milk price as a result of high production risk existing in livestock sector of Iran.