Price forecasting for agricultural products based on BP and RBF Neural Network

Abstract

In order to get the excellent accuracy for price forecast in the agriculture products market, the adaptive Radial Basis Function (RBF) Neural Network (NN) and Back Propagation (BP) NN are utilized to forecast the price of the agriculture products in this paper. Ten agriculture products, which extracted from Agricultural Bank of China at January, 2011 to December 2011, are selected to forecast the price about four weeks and compare the Mean Absolute Percentage Errors (MAPE) by RBF NN and BP NN respectively. Experiments demonstrate that the BP is better model which can get more than 99.6 percent accuracy than the RBF that can reduce the MAPE in the price forecast for the agriculture products market. Experiment results prove that this verdict is meaningful and useful to analyze and to research the price forecast in the agriculture products market.