  Given high-dimensional time series data (e.g., sensor data), how can we
detect anomalous events, such as system faults and attacks? More challengingly,
how can we do this in a way that captures complex inter-sensor relationships,
and detects and explains anomalies which deviate from these relationships?
Recently, deep learning approaches have enabled improvements in anomaly
detection in high-dimensional datasets; however, existing methods do not
explicitly learn the structure of existing relationships between variables, or
use them to predict the expected behavior of time series. Our approach combines
a structure learning approach with graph neural networks, additionally using
attention weights to provide explainability for the detected anomalies.
Experiments on two real-world sensor datasets with ground truth anomalies show
that our method detects anomalies more accurately than baseline approaches,
accurately captures correlations between sensors, and allows users to deduce
the root cause of a detected anomaly.
