Outliers treatment in support vector regression for financial time series prediction
Publication
, Chapter
Yang, H; Huang, K; Chan, L; King, I; Lyu, MR
January 1, 2004
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. © Springer-Verlas Berlin Heidelberg 2004.
Duke Scholars
DOI
Publication Date
January 1, 2004
Volume
3316
Start / End Page
1260 / 1265
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences
Citation
APA
Chicago
ICMJE
MLA
NLM
Yang, H., Huang, K., Chan, L., King, I., & Lyu, M. R. (2004). Outliers treatment in support vector regression for financial time series prediction (Vol. 3316, pp. 1260–1265). https://doi.org/10.1007/978-3-540-30499-9_196
Yang, H., K. Huang, L. Chan, I. King, and M. R. Lyu. “Outliers treatment in support vector regression for financial time series prediction,” 3316:1260–65, 2004. https://doi.org/10.1007/978-3-540-30499-9_196.
Yang H, Huang K, Chan L, King I, Lyu MR. Outliers treatment in support vector regression for financial time series prediction. In 2004. p. 1260–5.
Yang, H., et al. Outliers treatment in support vector regression for financial time series prediction. Vol. 3316, 2004, pp. 1260–65. Scopus, doi:10.1007/978-3-540-30499-9_196.
Yang H, Huang K, Chan L, King I, Lyu MR. Outliers treatment in support vector regression for financial time series prediction. 2004. p. 1260–1265.
DOI
Publication Date
January 1, 2004
Volume
3316
Start / End Page
1260 / 1265
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences