文献类型：会议论文

英文题名：WNN Prediction Model of Stock Price with Input Dimensions Reduced by Rough Set

作者：Huang Haiqing[1];Lou Yuanwei[2];Lei Lei[3];Li Huaping[4]

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;[4]Air Force Xian Flight Acad, Xian 710306, Shaanxi, 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 Education, Economics and Management Research (ICEEMR)

会议日期：MAY 29-31, 2017

会议地点：Singapore, SINGAPORE

语种：英文

外文关键词：Wavelet Neural Network; Rough Set; Attribute Reduction; Stock Price; Prediction

摘要：To improve the prediction ability of stock price, an integration prediction method based on Rough Set (RS) and Wavelet Neural Network (WNN) is proposed. First RS is used to reduce the dimensions of feature of stock price, then the WNN prediction model is established for stock price movement on the basis of feature dimension reduction; finally, the built model is applied to predict the stock price movement. The simulations on daily closing price index of SSE Composite Index indicate that, the proposed method has advantages of simple structure, strong implementation and good prediction accuracy with average correct rate 64%, and gets better stock price prediction in contrast with single neural network, genetic neural network and WNN.