Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approaches for this problem mostly employ either statistical models which cannot capture the non-linear relations well or conventional deep learning models (e.g., CNN and LSTM) that do not explicitly learn the pairwise correlations among variables. To overcome these limitations, we propose a novel method, correlation-aware spatial-temporal graph learning (termed CST-GL), for time series anomaly detection. CST-GL explicitly captures the pairwise correlations via a multivariate time series correlation learning module based on which a spatial-temporal graph neural network (STGNN) can be developed. Then, by employing a graph convolution network that exploits one- and multi-hop neighbor information, our STGNN component can encode rich spatial information from complex pairwise dependencies between variables. With a temporal module that consists of dilated convolutional functions, the STGNN can further capture long-range dependence over time. A novel anomaly scoring component is further integrated into CST-GL to estimate the degree of an anomaly in a purely unsupervised manner. Experimental results demonstrate that CST-GL can detect anomalies effectively in general settings as well as enable early detection across different time delays.

翻译：多变量时间序列异常检测在许多应用中至关重要，包括零售、交通、电网和水处理厂。现有方法大多采用统计模型（无法捕捉非线性关系）或传统深度学习模型（如CNN和LSTM，未能显式学习变量间的成对相关性）。为克服这些局限，我们提出了一种新方法——面向关联的时空图学习（简称CST-GL），用于时间序列异常检测。CST-GL通过多变量时间序列关联学习模块显式捕捉成对相关性，并基于此构建时空图神经网络（STGNN）。随后，利用能够挖掘单跳与多跳邻域信息的图卷积网络，STGNN组件可从变量间复杂成对依赖关系中编码丰富的空间信息。结合由膨胀卷积函数构成的时序模块，STGNN可进一步捕捉时间上的长程依赖。此外，CST-GL中集成了新颖的异常评分组件，能以纯无监督方式估计异常程度。实验结果表明，CST-GL不仅能有效检测一般场景中的异常，还能在不同时间延迟下实现早期检测。