Week-ahead Price Forecasting for Steel Market Based on RBF NN and ASW
In order to get the excellent accuracy for price forecast in the steel market, the adaptive Radial Basis Function (RBF) Neural Network (NN) and Adaptive Sliding Window (ASW) 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 RBF NN and ASW respectively. Experiments demonstrate that the ASW is better model which can get more than 97.3 percent accuracy than the RBF that can only obtain 93 percent accuracy in the price forecast for the steel products market. Experiment results prove that the proposed ASW is meaningful and useful to analyze and to research the price forecast in the steel products market.
price forecast steel market MAE RBF NN ASW
Bin Wu Quanyin Zhu
Faculty of Computer Engineering Huaiyin Institute of Technology Huaiart, Jiangsu Province, China Faculty of Computer Engineering Huaiyin Institute of Technology Huaian, Jiangsu Province, China
国际会议
北京
英文
729-732
2012-06-22（万方平台首次上网日期，不代表论文的发表时间）