Forecasting Wheat Price Using Backpropagation And NARX Neural Network
نویسنده
چکیده
--------------------------------------------------ABSTRACT-------------------------------------------------------This study aims to investigate suitable model and forecast future wheat price using backpropagation neural network (BPNN) and nonlinear autoregressive models with exogenous inputs (NARX) networks. The price of wheat was estimated using prices of 3 types of grains widely used in agriculture which are oats, barley and soybeans. The analysis was conducted using historical data on the prices from 1978 until 2012. Artificial neural networks are non-parametric statistical estimators, and thus have potential for use in estimation modeling. The variables selection criteria procedures are also developed to select a significance explanatory variable. The methods are compared to obtain the best model for predicting wheat price. Based on the results, the NARX model with 8 nodes in hidden layer and 4 tapped delay lines can be used as an alternative model predict wheat price.
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