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arXiv · 1906.06904

Normalizing flows for novelty detection in industrial time series data

Maximilian Schmidt·Marko Simic
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Abstract

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.

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Maximilian Schmidt, Marko Simic. 2019-06-17. Normalizing flows for novelty detection in industrial time series data. https://arxiv.org/abs/1906.06904

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