  Flow-based deep generative models learn data distributions by transforming a
simple base distribution into a complex distribution via a set of invertible
transformations. Due to the invertibility, such models can score unseen data
samples by computing their exact likelihood under the learned distribution.
This makes flow-based models a perfect tool for novelty detection, an anomaly
detection technique where unseen data samples are classified as normal or
abnormal by scoring them against a learned model of normal data. We show that
normalizing flows can be used as novelty detectors in time series. Two
flow-based models, Masked Autoregressive Flows and Free-form Jacobian of
Reversible Dynamics restricted by autoregressive MADE networks, are tested on
synthetic data and motor current data from an industrial machine and achieve
good results, outperforming a conventional novelty detection method, the Local
Outlier Factor.
