Computer Science > Machine Learning
arXiv:2603.09490 (cs)
[Submitted on 10 Mar 2026]
Title:Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection
View a PDF of the paper titled Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection, by David Baumgartner and 3 other authors
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Abstract:This paper introduces temporal-conditioned normalizing flows (tcNF), a novel framework that addresses anomaly detection in time series data with accurate modeling of temporal dependencies and uncertainty. By conditioning normalizing flows on previous observations, tcNF effectively captures complex temporal dynamics and generates accurate probability distributions of expected behavior. This autoregressive approach enables robust anomaly detection by identifying low-probability events within the learned distribution. We evaluate tcNF on diverse datasets, demonstrating good accuracy and robustness compared to existing methods. A comprehensive analysis of strengths and limitations and open-source code is provided to facilitate reproducibility and future research.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.09490 [cs.LG] |
| (or arXiv:2603.09490v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09490
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arXiv-issued DOI via DataCite
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Submission history
From: David Baumgartner [view email][v1] Tue, 10 Mar 2026 10:49:48 UTC (6,862 KB)
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View a PDF of the paper titled Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection, by David Baumgartner and 3 other authors
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