Abstract
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.
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Yang, H., Huang, K., Chan, L., King, I., Lyu, M.R. (2004). Outliers Treatment in Support Vector Regression for Financial Time Series Prediction. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_196
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DOI: https://doi.org/10.1007/978-3-540-30499-9_196
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
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