Wang Fengling, Jiang Yiyue, Zhang Rongjie, Wei Aimin, Xie Jingming, Pang Xiongwen
School of Artificial Intelligence, South China Normal University, Foshan 528000, China.
School of Computer Science, South China Normal University, Guangzhou 510555, China.
Sensors (Basel). 2025 Jan 1;25(1):190. doi: 10.3390/s25010190.
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.
多变量时间序列异常检测（MTSAD）能够有效识别和分析复杂系统中的异常行为，这在金融监测、工业设备故障检测和网络安全等领域尤为重要。MTSAD需要同时分析时间依赖性和变量间关系，这促使研究人员开发专门的深度学习模型来检测异常模式。在本文中，我们对用于多变量时间序列异常检测方法的深度学习最新技术进行了结构化和全面的概述。首先，我们从学习范式和深度学习模型的角度提出了异常检测策略的分类法，然后进行了系统综述，强调了它们的优缺点。我们还整理了用于时间序列异常检测的公共数据集及其各自的应用领域。最后，确定了MTSAD未来研究的开放性问题。
