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Anomaly Detection in Time Series

Heraldo Borges 1 Reza Akbarinia 1 Florent Masseglia 1
1 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Data mining has become an important task for researchers in the past few years, including detecting anomalies that may represent events of interest. The problem of anomaly detection refers to discovering the patterns that do not conform to expected behavior. This paper analyzes recent studies on the detection of anomalies in time series. The goal is to provide an introduction to anomaly detection and a survey of recent research and challenges. The article is divided into three main parts. First, the main concepts are presented. Then, the anomaly detection task is defined. Afterward, the main approaches and strategies to solve the problem are presented.
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Contributor : Reza Akbarinia Connect in order to contact the contributor
Submitted on : Thursday, September 30, 2021 - 11:33:00 AM
Last modification on : Friday, October 22, 2021 - 3:07:19 PM


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  • HAL Id : lirmm-03359500, version 1



Heraldo Borges, Reza Akbarinia, Florent Masseglia. Anomaly Detection in Time Series. Transactions on Large-Scale Data- and Knowledge-Centered Systems, Springer Berlin / Heidelberg, In press. ⟨lirmm-03359500⟩



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