WNN Prediction Model of Stock Price with Input Dimensions Reduced by Rough Set
Authors
Haiqing Huang, Yuanwei Lou, Lei Lei, Huaping Li
Corresponding Author
Haiqing Huang
Available Online May 2017.
- DOI
- 10.2991/iceemr-17.2017.27How to use a DOI?
- Keywords
- Wavelet Neural Network, Rough Set, Attribute Reduction, Stock Price, Prediction
- Abstract
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.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Haiqing Huang AU - Yuanwei Lou AU - Lei Lei AU - Huaping Li PY - 2017/05 DA - 2017/05 TI - WNN Prediction Model of Stock Price with Input Dimensions Reduced by Rough Set BT - Proceedings of the 2017 International Conference on Education, Economics and Management Research (ICEEMR 2017) PB - Atlantis Press SP - 107 EP - 110 SN - 2352-5398 UR - https://doi.org/10.2991/iceemr-17.2017.27 DO - 10.2991/iceemr-17.2017.27 ID - Huang2017/05 ER -