mlsanityData to 7 Oct 2026

Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection

arXiv 2603.09490 ↗First posted 10 Mar 2026cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 41 references that are in the map. First posted on arXiv 10 Mar 2026.

Builds on

14 of 41 references.

References · 14

  1. 2016Density estimation using Real NVPcited by 2,029
  2. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  3. 2018WaveGlow: A Flow-based Generative Network for Speech Synthesiscited by 308
  4. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  5. 2020Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flowscited by 142
  6. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
  7. 2023Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecastingcited by 102
  8. 2022Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Seriescited by 42
  9. 2016Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Modelcited by 33
  10. 2023Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Seriescited by 24
  11. 2019Neural Density Estimation and Likelihood-free Inferencecited by 23
  12. 2023Label-Free Multivariate Time Series Anomaly Detectioncited by 14
2 more
  1. 2023The voraus-AD Dataset for Anomaly Detection in Robot Applicationscited by 7
  2. 2019Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State Networkcited by 6

Built on by

Not cited yet.