Outliers treatment in support vector regression for financial time series prediction

被引：0
|
作者：
Yang, HQ [1 ]
Huang, KZ [1 ]
Chan, LW [1 ]
King, I [1 ]
Lyu, MR [1 ]
机构：
[1] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China
来源：
NEURAL INFORMATION PROCESSING | 2004年 / 3316卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Recently, the Support Vector Regression (SVR) has been applied in the financial time series prediction. The financial data are usually highly noisy and contain outliers. Detecting outliers and deflating their influence are important but hard problems. In this paper, we propose a novel "two-phase" SVR training algorithm to detect outliers and reduce their negative impact. Our experimental results on three indices: Hang Seng Index, NASDAQ, and FSTE 100 index show that the proposed "two-phase" algorithm has improvement on the prediction.
引用
收藏
页码：1260 / 1265
页数：6
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