Analysis of Time Series Novelty Detection Strategies for Synthetic and Real Data

Authors:
André Paoliello Modenesi;Antônio Pádua Braga
Affiliations:
Department of Electronic Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil;Department of Electronic Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil
Venue:
Neural Processing Letters
Year:
2009

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Abstract

Novelty detection is inspired by animal behavior and can be applied to a variety of practical situations. By automatically bringing attention to anomalous events, without the need of previously defining those events, it can perform certain tasks that would otherwise require human scrutiny. This paper reviews some approaches to the problem when applied to the time series analysis problem.