  Multivariate time series anomaly detection (MTAD) plays a vital role in a
wide variety of real-world application domains. Over the past few years, MTAD
has attracted rapidly increasing attention from both academia and industry.
Many deep learning and graph learning models have been developed for effective
anomaly detection in multivariate time series data, which enable advanced
applications such as smart surveillance and risk management with unprecedented
capabilities. Nevertheless, MTAD is facing critical challenges deriving from
the dependencies among sensors and variables, which often change over time. To
address this issue, we propose a coupled attention-based neural network
framework (CAN) for anomaly detection in multivariate time series data
featuring dynamic variable relationships. We combine adaptive graph learning
methods with graph attention to generate a global-local graph that can
represent both global correlations and dynamic local correlations among
sensors. To capture inter-sensor relationships and temporal dependencies, a
convolutional neural network based on the global-local graph is integrated with
a temporal self-attention module to construct a coupled attention module. In
addition, we develop a multilevel encoder-decoder architecture that
accommodates reconstruction and prediction tasks to better characterize
multivariate time series data. Extensive experiments on real-world datasets
have been conducted to evaluate the performance of the proposed CAN approach,
and the results show that CAN significantly outperforms state-of-the-art
baselines.
