  In this work, we propose GLUE (Graph Deviation Network with Local Uncertainty
Estimation), building on the recently proposed Graph Deviation Network (GDN).
GLUE not only automatically learns complex dependencies between variables and
uses them to better identify anomalous behavior, but also quantifies its
predictive uncertainty, allowing us to account for the variation in the data as
well to have more interpretable anomaly detection thresholds. Results on two
real world datasets tell us that optimizing the negative Gaussian log
likelihood is reasonable because GLUE's forecasting results are at par with GDN
and in fact better than the vector autoregressor baseline, which is significant
given that GDN directly optimizes the MSE loss. In summary, our experiments
demonstrate that GLUE is competitive with GDN at anomaly detection, with the
added benefit of uncertainty estimations. We also show that GLUE learns
meaningful sensor embeddings which clusters similar sensors together.
