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Book Sections Year : 2021

Anomaly Detection in Time Series

Heraldo Borges
Reza Akbarinia
Florent Masseglia

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 finding samples 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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Dates and versions

lirmm-03359500 , version 1 (30-09-2021)

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Heraldo Borges, Reza Akbarinia, Florent Masseglia. Anomaly Detection in Time Series. Transactions on Large-Scale Data- and Knowledge-Centered Systems L, LNCS. TLDKS -12930, , pp.46-62, 2021, Lecture Notes in Computer Science. Transactions on Large-Scale Data- and Knowledge-Centered Systems, 978-3-662-64553-6. ⟨10.1007/978-3-662-64553-6_3⟩. ⟨lirmm-03359500⟩
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