Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows

arXiv:2603.11756v2 Announce Type: replace-cross Abstract: Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing observed data likelihood. However, likelihood in observation space measures marginal density rather than conformity to structured temporal dynamics, and therefore can assign high probability to anomalous or out-of-distribution samples. We address this structural limitation by relocating the notion of anomaly to a prescribed latent space. We introduce explicit inductive biases in conditional normalizing flows, modeling time-series

Source: arXiv cs.LG — read the full report at the original publisher.

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