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By considering the characteristics of agricultural products prices such as great fluctuations, nonstationarity and nonlinearity, a BP neural network model was designed for forecasting and verified by real data from some agricultural products wholesale markets. In order to improve the prediction accuracy, a combination model of BP neural network and time series prediction was constructed to forecast prices of agricultural products in wholesale markets. After learning from sample data, it can better forecast the trend and fluctuations of the agricultural products wholesale prices, and with those real data better prediction results were attained. The combined model provides an important method for predicting prices in agricultural products wholesale markets.
Date of Conference: 26-27 Dec. 2009