Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series
Authors: Maximilian Sölch, Justin Bayer, Marvin Ludersdorfer, Patrick van der Smagt

Abstract: Approximate variational inference has shown to be a powerful tool for modeling unknown, complex probability distributions. Recent advances in the field allow us to learn probabilistic sequence models. We apply a Stochastic Recurrent Network (STORN) to learn robot time series data. Our evaluation demonstrates that we can robustly detect anomalies both off- and on-line.