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Detection of Anomalies and Novelties in Time Series with Self-Organizing Networks

Aguayo, Leonardo ; Barreto, Guilherme A.

This paper introduces the DANTE project: Detection of Anomalies and Novelties in Time sEries with self-organizing networks. The goal of this project is to evaluate several self-organizing networks in the detection of anomalies/novelties in dynamic data patterns. For this purpose, we first describe three standard clustering-based approaches which uses well-known self-organizing neural architectures, such as the SOM and the Fuzzy ART algorithms, and then present a novel approach based on the Operator Map (OPM) network. The OPM is a generalization of the SOM where neurons are regarded as temporal filters for dynamic patters. The OPM is used to build local adaptive filters for a given nonstationary time series. Non-parametric confidence intervals are then computed for the residuals of the local models and used as decision thresholds for detecting novelties/anomalies. Computer simulations are carried out to compare the performances of the aforementioned algorithms.

Schlagwörter: time series analysis, novelty detection, operator map, adaptive filtering, self-organizing networks
Beteiligte Einrichtung: Technische Fakultät, Arbeitsgruppen der Informatik
DDC-Sachgruppe: Datenverarbeitung, Informatik

Aguayo, Leonardo ; Barreto, Guilherme A.  (2007)  Detection of Anomalies and Novelties in Time Series with Self-Organizing Networks.

URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2007/132

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 Letzte Änderung: 15.2.2011
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