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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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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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Wang HQiu XXiong YTan X(2025)AutoGRNInformation Fusion10.1016/j.inffus.2024.102836117:COnline publication date: 1-May-2025
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