An improved method of support vector machine and its applications to financial time series forecasting

被引：8
|
作者：
Liang, YC [1 ]
Sun, YF
机构：
[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
[2] Jilin Univ, Coll Math, Changchun 130012, Peoples R China
来源：
PROGRESS IN NATURAL SCIENCE-MATERIALS INTERNATIONAL | 2003年 / 13卷 / 09期
关键词：
support vector machine (SVM); data-dependent; information geometry; conformal mapping; financial time series; stock price prediction;
D O I：
10.1080/10020070312331344260
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
A novel method for kernel function of support vector machine is presented based on the information geometry theory. The kernel function is modified using a conformal mapping to make the kernel data-dependent so as to increase the ability of predicting high noise data of the method. Numerical simulations demonstrate the effectiveness of the method. Simulated results on the prediction of the stock price show that the improved approach possesses better forecasting precision and ability of generalization than the conventional models.
引用
收藏
页码：696 / 700
页数：5
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