A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions
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 tempor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/190 |
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| author | Fengling Wang Yiyue Jiang Rongjie Zhang Aimin Wei Jingming Xie Xiongwen Pang |
| author_facet | Fengling Wang Yiyue Jiang Rongjie Zhang Aimin Wei Jingming Xie Xiongwen Pang |
| author_sort | Fengling Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-12a790b7c48b48789eaa4cafed0c5d11 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-12a790b7c48b48789eaa4cafed0c5d112025-01-10T13:21:10ZengMDPI AGSensors1424-82202025-01-0125119010.3390/s25010190A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and DirectionsFengling Wang0Yiyue Jiang1Rongjie Zhang2Aimin Wei3Jingming Xie4Xiongwen Pang5School of Artificial Intelligence, South China Normal University, Foshan 528000, ChinaSchool of Artificial Intelligence, South China Normal University, Foshan 528000, ChinaSchool of Computer Science, South China Normal University, Guangzhou 510555, ChinaSchool of Architectural Engineering, Guangzhou Panyu Polytechnic College, Guangzhou 511483, ChinaDoctoral Workstation, Guangdong Songshan Polytechnic, Shaoguan 512126, ChinaSchool of Computer Science, South China Normal University, Guangzhou 510555, ChinaMultivariate 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.https://www.mdpi.com/1424-8220/25/1/190anomaly detectiondeep learning networkmultivariate time series |
| spellingShingle | Fengling Wang Yiyue Jiang Rongjie Zhang Aimin Wei Jingming Xie Xiongwen Pang A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions Sensors anomaly detection deep learning network multivariate time series |
| title | A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions |
| title_full | A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions |
| title_fullStr | A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions |
| title_full_unstemmed | A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions |
| title_short | A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions |
| title_sort | survey of deep anomaly detection in multivariate time series taxonomy applications and directions |
| topic | anomaly detection deep learning network multivariate time series |
| url | https://www.mdpi.com/1424-8220/25/1/190 |
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