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Novelty Detection with One-Class Support Vector Machines

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 Green open access

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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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