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ICONIP
2004

Outliers Treatment in Support Vector Regression for Financial Time Series Prediction

10 years 4 months ago
Outliers Treatment in Support Vector Regression for Financial Time Series Prediction
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.
Haiqin Yang, Kaizhu Huang, Laiwan Chan, Irwin King
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where ICONIP
Authors Haiqin Yang, Kaizhu Huang, Laiwan Chan, Irwin King, Michael R. Lyu
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