Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection

arXiv cs.LG / 3/11/2026

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The paper proposes temporal-conditioned normalizing flows (tcNF), a novel framework designed for anomaly detection in multivariate time series data by effectively modeling temporal dependencies and uncertainty.
TcNF conditions normalizing flows on past observations to capture complex temporal dynamics and produce accurate probability distributions of expected behaviors in an autoregressive manner.
This approach enables robust detection of anomalies by identifying low-probability events within the learned distributions.
The model was evaluated on various datasets, showing superior accuracy and robustness compared to existing methods.
The authors provide a thorough analysis of the method's strengths and weaknesses, along with open-source code to support reproducibility and encourage further research.

Computer Science > Machine Learning

arXiv:2603.09490 (cs)

[Submitted on 10 Mar 2026]

Title:Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection

Authors:David Baumgartner, Helge Langseth, Kenth Engø-Monsen, Heri Ramampiaro

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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 Focus to learn more arXiv-issued DOI via DataCite

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From: David Baumgartner [view email]
[v1] Tue, 10 Mar 2026 10:49:48 UTC (6,862 KB)

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Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection

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Computer Science > Machine Learning

Title:Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection

Submission history