3 papers
cs.AI2026
Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows
David Baumgartner, Eliezer de Souza da Silva, Iñigo Urteaga
Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing observed data likelihood. However, likelihood in observation space meas…
cs.AI2026
CINDI: Conditional Imputation and Noisy Data Integrity with Flows in Power Grid Data
David Baumgartner, Helge Langseth, Heri Ramampiaro
Real-world multivariate time series, particularly in critical infrastructure such as electrical power grids, are often corrupted by noise and anomalies that degrade the performance…
cs.LG2026
Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection
David Baumgartner, Helge Langseth, Kenth Engø-Monsen +1
This paper introduces temporal-conditioned normalizing flows (tcNF), a novel framework that addresses anomaly detection in time series data with accurate modeling of temporal depen…