Paper Info
Title
Application of feature-weighted Support Vector regression using grey correlation degree to stock price forecasting.
Abstract
A feature-weighted Support Vector Machine regression algorithm is introduced in this paper. We note that the classical SVM is based on the assumption that all the features of the sample points supply the same contribution to the target output value. However, this assumption is not always true in real problems. In the proposed new algorithm, we give different weight values to different features of the samples in order to improve the performance of SVM. In our algorithm, firstly, a measure named grey correlation degree is applied to evaluate the correlation between each feature and the target problem, and then the values of the grey correlation degree are used as weight values assigned to the features. The proposed method is tested on sample stock data sets selected from China Shenzhen A-share market. The result shows that the new version of SVM can improve the accuracy of the prediction.
Year
DOI
Venue
2013
10.1007/s00521-012-0969-3
Neural Computing and Applications
Keywords
DocType
Volume
Support vector machines, Regression, Grey correlation degree, Stock price forecasting
Journal
22
Issue
ISSN
Citations 
Supplement-1
1433-3058
2
PageRank 
References 
Authors
0.42
14
2
Name
Order
Citations
PageRank
James N. K. Liu152944.35
Yan-Xing Hu2795.74