Shawe-Taylor, J;
Zlicar, B;
(2015)
Novelty Detection with One-Class Support Vector Machines.
In: Morlini, I and Minerva, T and Vichi, M, (eds.)
Advances in Statistical Models for Data Analysis.
(pp. pp. 231-257).
Springer, Cham
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Abstract
In this paper we apply one-class support vector machine (OC-SVM) to identify potential anomalies in financial time series. We view anomalies as deviations from a prevalent distribution which is the main source behind the original signal. We are interested in detecting changes in the distribution and the timing of the occurrence of the anomalous behaviour in financial time series. The algorithm is applied to synthetic and empirical data. We find that our approach detects changes in anomalous behaviour in synthetic data sets and in several empirical data sets. However, it requires further work to ensure a satisfactory level of consistency and theoretical rigour.
| Type: | Proceedings paper |
|---|---|
| Title: | Novelty Detection with One-Class Support Vector Machines |
| Event: | 9th Biannual Meeting of the Classification-and-Data-Analysis-Group (CLADAG) of the Italian-Statistical-Society |
| Location: | Modena, ITALY |
| Dates: | 18 September 2013 - 20 September 2013 |
| ISBN-13: | 978-3-319-17376-4 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-319-17377-1_24 |
| Publisher version: | https://doi.org/10.1007/978-3-319-17377-1_24 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Financial time series Novelty detection One-class SVM |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1476410 |
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