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Transactions on Large-Scale Data- and Knowledge-Centered Systems L pp 46–62Cite as

Anomaly Detection in Time Series

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Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 12930))

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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Authors and Affiliations

  1. Inria & LIRMM, Univ. Montpellier, Montpellier, France

    Heraldo Borges, Reza Akbarinia & Florent Masseglia

Authors
  1. Heraldo Borges
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  2. Reza Akbarinia
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  3. Florent Masseglia
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Corresponding authors

Correspondence to Heraldo Borges , Reza Akbarinia or Florent Masseglia .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. IFS, Technical University of Vienna, Vienna, Austria

    A Min Tjoa

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Borges, H., Akbarinia, R., Masseglia, F. (2021). Anomaly Detection in Time Series. In: Hameurlain, A., Tjoa, A.M. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems L. Lecture Notes in Computer Science(), vol 12930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-64553-6_3

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  • DOI: https://doi.org/10.1007/978-3-662-64553-6_3

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  • Print ISBN: 978-3-662-64552-9

  • Online ISBN: 978-3-662-64553-6

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