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Deep learning for anomaly detection in multivariate time series: : Approaches, applications, and challenges

Authors:

Jason J. JungAuthors Info & Claims

Volume 91, Issue C

Pages 93 - 102

https://doi.org/10.1016/j.inffus.2022.10.008

Published: 01 March 2023 Publication History

Abstract

Anomaly detection has recently been applied to various areas, and several techniques based on deep learning have been proposed for the analysis of multivariate time series. In this study, we classify the anomalies into three types, namely abnormal time points, time intervals, and time series, and review the state-of-the-art deep learning techniques for the detection of each of these types. Long short-term memory and autoencoders are the most commonly used methods for detecting abnormal time points and time intervals. In addition, some studies have implemented dynamic graphs to examine relational features between the time series and detect abnormal time intervals. However, anomaly detection still faces some limitations and challenges, such as the explainability of anomalies. Many studies have focused only on anomaly detection methods but failed to consider the reasons for the anomalies. Therefore, increasing the explainability of anomalies is an important research topic in anomaly detection.

Highlights

•

The methods for anomaly detection on multivariate time series are reviewed.

•

The applications based on anomaly detection are summarized.

•

The open-access time series datasets for anomaly detection are provided.

•

The open issues for anomaly detection on multivariate time series are presented.

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Cited By

Zhang YJiang ZPeng CZhu XWang G(2024)Management Analysis Method of Multivariate Time Series Anomaly Detection in Financial Risk AssessmentJournal of Organizational and End User Computing10.4018/JOEUC.34209436:1(1-19)Online publication date: 16-Apr-2024
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Tian YWang WHe J(2024)An IIoT Temporal Data Anomaly Detection Method Combining Transformer and Adversarial TrainingInternational Journal of Information Security and Privacy10.4018/IJISP.34330618:1(1-28)Online publication date: 15-May-2024
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Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges

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cover image Information Fusion

Information Fusion Volume 91, Issue C

Mar 2023

758 pages

ISSN:1566-2535

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Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

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Published: 01 March 2023

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Cited By

Zhang YJiang ZPeng CZhu XWang G(2024)Management Analysis Method of Multivariate Time Series Anomaly Detection in Financial Risk AssessmentJournal of Organizational and End User Computing10.4018/JOEUC.34209436:1(1-19)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.4018/JOEUC.342094
Tian YWang WHe J(2024)An IIoT Temporal Data Anomaly Detection Method Combining Transformer and Adversarial TrainingInternational Journal of Information Security and Privacy10.4018/IJISP.34330618:1(1-28)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.4018/IJISP.343306
Khatraty YMellouli NDiallo MNanne M(2024)Smart Agriculture Framework with Digital Twin: A Monitoring Model Based on Clustering and prediction of Multivariate Time SeriesProceedings of the 2nd International Workshop on Middleware for Digital Twins10.1145/3702636.3703759(13-18)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3702636.3703759
Zeng HChen NDeng T(2024)LSTD-MTS: Anomaly Detection with Capturing Long-Term Spatio-Temporal Dependence for Multi-dimensional Time SeriesProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3671383(397-406)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3671016.3671383
Wang RMou XYang RGao KLiu PLiu CWo TLiu XBaeza-Yates RBonchi F(2024)CutAddPaste: Time Series Anomaly Detection by Exploiting Abnormal KnowledgeProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671739(3176-3187)Online publication date: 25-Aug-2024
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Xue BGao XLi BZhai FLu JYu JFu SXiao C(2024)A robust multi-scale feature extraction framework with dual memory module for multivariate time series anomaly detectionNeural Networks10.1016/j.neunet.2024.106395177:COnline publication date: 1-Sep-2024
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Yu JGao XLi BZhai FLu JXue BFu SXiao C(2024)A filter-augmented auto-encoder with learnable normalization for robust multivariate time series anomaly detectionNeural Networks10.1016/j.neunet.2023.11.047170:C(478-493)Online publication date: 12-Apr-2024
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Yu JGao XZhai FLi BXue BFu SChen LMeng Z(2024)An adversarial contrastive autoencoder for robust multivariate time series anomaly detectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123010245:COnline publication date: 2-Jul-2024
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