Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series

Maximilian Sölch, Justin Bayer, Marvin Ludersdorfer, Patrick van der Smagt

Feb 18, 2016 (modified: Feb 18, 2016) ICLR 2016 workshop submission readers: everyone
  • 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.
  • Conflicts: tum.de, fortiss.org, brml.org

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