A Review on Outlier/Anomaly Detection in Time Series Data

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...

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Bibliographic Details
Published inACM computing surveys Vol. 54; no. 3; pp. 1 - 33
Main Authors Blázquez-García, Ane, Conde, Angel, Mori, Usue, Lozano, Jose A.
Format Journal Article
LanguageEnglish
Published Baltimore Association for Computing Machinery 01.04.2022
Subjects
Anomalies
Computer science
Data analysis
Data collection
Outliers (statistics)
Taxonomy
Time series
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Summary: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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ISSN:0360-0300
1557-7341
DOI:10.1145/3444690