Optimum prediction and forecasting of wheat demand in Iran

Reza Babazadeh, Meisam Shamsi and Fatemeh Shafipour

International Journal of Applied Management Science, 2021, vol. 13, issue 2, 141-151

Abstract: Wheat is the staple food source in most countries and is grown in bad climatic conditions such as cold areas. Wheat contains about 55% carbohydrates and 20% calories. Optimum prediction of wheat demand would help policy makers to take optimum strategic decisions about the amount of domestic wheat production, import, and export for mid and long terms. In this study, firstly, the factors affecting demand for wheat are identified according to market analysis. Then, artificial neural network (ANN) method is employed for optimum forecasting of wheat demand in Iran. Different regression methods are used to justify the efficiency of the ANN model. The mean absolute percentage error (MAPE) of the ANN method is achieved equal to 4.64% which shows about 95% precision of the ANN method. According to acquired results, the ANN method could be efficiently applied for wheat demand prediction in order to take appropriate related strategic decisions.

Keywords: wheat demand; forecasting; ANN; artificial neural network; regression models. (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:ids:injams:v:13:y:2021:i:2:p:141-151

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