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2012 International Conference on Computer Science and Service System
In order to get the excellent accuracy for price forecast in the steel market, the adaptive Radial Basis Function (RBF) Neural Network (NN) model, Back Propagation (BP) NN model and Sliding Window (SW) model are utilized to forecast the price of the steel products in this paper. Eight steel products, which extracted from Shanghai Baoshan steel market of China at January, 2011 to December 2011, are selected to forecast the price about one week and compare the Mean Absolute Errors (MAE) by RBFdoi:10.1109/csss.2012.459 fatcat:mr4d6yxnibbgrgrfmv7ualbpmy