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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Published in:ACM computing surveys 2022-04, Vol.54 (3), p.1-33
Main Authors: Blázquez-García, Ane, Conde, Angel, Mori, Usue, Lozano, Jose A.
Format: Article
Language:English
Subjects:
Anomalies
Computer science
Data analysis
Data collection
Outliers (statistics)
Taxonomy
Time series
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description 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.
doi_str_mv 10.1145/3444690
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source Business Source Ultimate【Trial: -2024/12/31】【Remote access available】; Association for Computing Machinery:Jisc Collections:ACM OPEN Journals 2023-2025 (reading list)
subjects Anomalies
Computer science
Data analysis
Data collection
Outliers (statistics)
Taxonomy
Time series
title A Review on Outlier/Anomaly Detection in Time Series Data
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