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A Review on Outlier/Anomaly Detection in Time Series Data

Authors:

Ane Blázquez-García,

Jose A. LozanoAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 3

Article No.: 56, Pages 1 - 33

https://doi.org/10.1145/3444690

Published: 17 April 2021 Publication History

Abstract

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on unsupervised outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.

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Published In

ACM Computing Surveys Volume 54, Issue 3

April 2022

836 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3461619

Editor:
Albert Zomaya
University of Sydney, Australia

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Published: 17 April 2021

Accepted: 01 December 2020

Revised: 01 July 2020

Received: 01 January 2020

Published in CSUR Volume 54, Issue 3

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Basque Government through the research group
Spanish Ministry of Science, Innovation, and Universities: BCAM Severo Ochoa accreditation
Basque Government through the BERC 2018-2021 program and research group
Elkartek program under the DIGITAL project of the Basque Government

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