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| Multivariable time series data anomaly detection method based on spatiotemporal graph attention network |
Gang XIAO( ),Dapeng LU,Wenbo ZHENG,Zhenbo CHENG,Yuanming ZHANG*() |
| College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China |
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Abstract Existing anomaly detection methods of time series data focus on extracting the temporal variation features, while the spatial dependency features between multiple variables are ignored. To address this problem, a detection method based on a spatiotemporal graph attention network was proposed. The original multivariate time series data were transformed into a time-series graph with spatiotemporal dependencies, and a spatiotemporal graph attention network was designed to separately extract the temporal variation features and spatial dependency features. The periodic patterns of fused spatiotemporal features were learned by a multilayer perceptron, and an anomaly detection was performed based on the anomaly scores between prediction values and observation values. Experimental results on public datasets showed that the proposed method significantly outperformed state-of-the-art baseline methods in terms of anomaly detection accuracy and robustness.
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Received: 08 October 2024
Published: 27 October 2025
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| Fund: 浙江省“尖兵”“领雁”研发攻关项目（2023C01022）；国家自然科学基金资助项目（62476248）. |
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Corresponding Authors:
Yuanming ZHANG
E-mail: xg@zjut.edu.cn;zym@zjut.edu.cn
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基于时空图注意力网络的多变量时序数据异常检测方法
现有时序数据异常检测方法侧重于提取时序数据的时间变化特征，忽略了多变量之间的空间依赖特征，为此提出基于时空图注意力网络的检测方法. 将多变量时序数据转换为具有时空依赖关系的时序图结构数据，设计时空图注意力网络分别提取时序图结构数据的时间变化特征和空间依赖特征. 通过多层感知机学习时空特征的周期性模式，根据时序数据的预测值与观测值的异常分数进行异常检测. 在公开数据集上的实验结果表明，所提方法在异常检测精准度和鲁棒性方面显著优于现有先进的基线方法.
关键词：
异常检测,
时序图,
图深度学习,
多变量时序数据,
时空特征融合
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