Learning Graph Neural Networks for Multivariate Time Series Anomaly Detection
This work addresses anomaly detection in multivariate time series for applications like sensor monitoring, but it is incremental as it builds on the existing Graph Deviation Network.
The authors tackled multivariate time series anomaly detection by proposing GLUE, which learns dependencies between variables and quantifies predictive uncertainty, achieving competitive anomaly detection performance with GDN while providing uncertainty estimates.
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.