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Time-Series Novelty Detection Using OneClass Support Vector (2003)
| Venue: | Machines,” Proc. Int’l Joint Conf. Neural Networks (IJCNN ’03 |
| Citations: | 28 - 0 self |
BibTeX
@INPROCEEDINGS{Ma03time-seriesnovelty,
author = {Junshui Ma and Simon Perkins},
title = {Time-Series Novelty Detection Using OneClass Support Vector},
booktitle = {Machines,” Proc. Int’l Joint Conf. Neural Networks (IJCNN ’03},
year = {2003},
pages = {1741--1745}
}
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Abstract
Abstract- Time-series novelty detection, or anomaly detection, refers to the automatic identification of novel or abnormal events embedded in normal time-series points. Although it is a challenging topic in data mining, it has been acquiring increasing attention due to its huge potential for immediate applications. I n this paper, a new algorithm for time-series novelty detection based on one-class support vector machines (SVMs) is proposed. The concepts o f phase and projected phase spaces are first introduced, which allows us to convert a time-series into a set of vectors in the (projected) phase spaces. Then we interpret novel events in time-series as outliers of the “normal ” distribution of the converted vectors in the (projected) phase spaces. One-class SVMs are employed as the outlier detectors. I n order to obtain robust detection results, a technique to combine intermediate results at different phase spaces is also proposed, Experiments on hotb synthetic and measured data are presented to demonstrate the promising performance of the new algorithm. I.
Keyphrases
phase space new algorithm huge potential outlier detector converted vector one-class svms anomaly detection intermediate result one-class support vector machine abstract time-series novelty detection projected phase space promising performance automatic identification robust detection result normal distribution data mining immediate application different phase space abnormal event normal time-series point novel event
