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Review
. 2025 Jan 1;25(1):190.
doi: 10.3390/s25010190.

A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions

Fengling Wang  1 , Yiyue Jiang  1 , Rongjie Zhang  2 , Aimin Wei  3 , Jingming Xie  4 , Xiongwen Pang  2
Affiliations
Review

A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions

Fengling Wang et al. Sensors (Basel). .

Abstract

Multivariate time series anomaly detection (MTSAD) can effectively identify and analyze anomalous behavior in complex systems, which is particularly important in fields such as financial monitoring, industrial equipment fault detection, and cybersecurity. MTSAD requires simultaneously analyze temporal dependencies and inter-variable relationships have prompted researchers to develop specialized deep learning models to detect anomalous patterns. In this paper, we conducted a structured and comprehensive overview of the latest techniques in deep learning for multivariate time series anomaly detection methods. Firstly, we proposed a taxonomy for the anomaly detection strategies from the perspectives of learning paradigms and deep learning models, and then provide a systematic review that emphasizes their advantages and drawbacks. We also organized the public datasets for time series anomaly detection along with their respective application domains. Finally, open issues for future research on MTSAD were identified.

Keywords: anomaly detection; deep learning network; multivariate time series.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 4
Figure 4
The general pipeline for MTSAD using deep learning models. Given a source data, we first process the source data using a data processing module that performs basic data cleaning and normalization tasks. Subsequently, we utilize the anomaly detection strategies from the perspectives of learning paradigms and deep learning models to obtain representations for executing anomaly detection tasks across different application domains.
Figure 1
Figure 1
The overall classification framework diagram for multivariate time series anomaly types.
Figure 2
Figure 2
(a) Point-wise anomalies, the red dots indicate anomalies, and (b) Patten-wise anomalies, the red areas represent anomalies.
Figure 3
Figure 3
(a) Global intermetric anomalies. (b) The red-highlighted area on the left indicates local intermetric anomalies, while the red-highlighted area on the right indicates temporal-local intermetric anomalies.
Figure 5
Figure 5
The examples of each type of anomaly criteria: (a) a forecasting loss; (b) a reconstruction loss; and (c) a contrastive loss.
See this image and copyright information in PMC

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