  Many multivariate time series anomaly detection frameworks have been proposed
and widely applied. However, most of these frameworks do not consider intrinsic
relationships between variables in multivariate time series data, thus ignoring
the causal relationship among variables and degrading anomaly detection
performance. This work proposes a novel framework called CGAD, an entropy
Causal Graph for multivariate time series Anomaly Detection. CGAD utilizes
transfer entropy to construct graph structures that unveil the underlying
causal relationships among time series data. Weighted graph convolutional
networks combined with causal convolutions are employed to model both the
causal graph structures and the temporal patterns within multivariate time
series data. Furthermore, CGAD applies anomaly scoring, leveraging median
absolute deviation-based normalization to improve the robustness of the anomaly
identification process. Extensive experiments demonstrate that CGAD outperforms
state-of-the-art methods on real-world datasets with a 15% average improvement
based on three different multivariate time series anomaly detection metrics.

Machine Learning;Artificial Intelligence