Related papers: Learning Graph Neural Networks for Multivariate Time Series Anomaly Detection

Learning Graph Neural Networks for Multivariate Time Series Anomaly Detection

URL: http://arxiv.org/abs/2111.08082v1
Date: Mon, 15 Nov 2021 21:05:58 GMT
Title: Learning Graph Neural Networks for Multivariate Time Series Anomaly Detection
Authors: Saswati Ray, Sana Lakdawala, Mononito Goswami, Chufan Gao
Abstract summary: We propose GLUE (Graph Deviation Network with Local Uncertainty Estimation) GLUE learns complex dependencies between variables and uses them to better identify anomalous behavior. We also show that GLUE learns meaningful sensor embeddings which clusters similar sensors together.
Score: 8.688578727646409
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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