文献类型：会议论文

英文题名：Neural Network Prediction of Stock Price Trend Based on RS with Entropy Discretization

作者：Huang Haiqing[1];Gan Xusheng[2];Lei Lei[3]

第一作者：Huang Haiqing

通讯作者：Huang, HQ[1]

机构：[1]XiJing Coll, Xian 710123, Shaanxi, Peoples R China;[2]Air Force Engn Univ, Air Traff Control & Nav Coll, Xian 710051, Shaanxi, Peoples R China;[3]Henan Univ Econ & Law, Sch Business Adm, Zhengzhou 050046, Henan, Peoples R China

第一机构：XiJing Coll, Xian 710123, Shaanxi, Peoples R China

通讯机构：[1]corresponding author), XiJing Coll, Xian 710123, Shaanxi, Peoples R China.

会议论文集：International Conference on Materials, Energy, Civil Engineering and Computer (MATECC)

会议日期：SEP 27-29, 2017

会议地点：Sanya, PEOPLES R CHINA

语种：英文

外文关键词：BP Neural Network; Rough Set; Stock Price Trend; Discretization; Attribute Reduction

摘要：To accurately predict the stock price trend, an integration prediction method based on Rough Set (RS) and BP neural network are proposed. In the method, RS is firstly applied to reduce the features of stock price trend, and an entropy-based discretization algorithm is introduced to process the continuous attribute data, then, on this basis, BP neural network is used to establish the prediction model of stock price trend. The validation result indicates that, by RS attribute reduction, the prediction model of BP neural network for stock price trend can be simplified with performance improvement. The prediction result is better than those of traditional neural network and RBF neural network. This verifies its feasibility and effectiveness.