Variational Inference for On-line Anomaly Detection in High-Dimensional   Time Series
  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 models of sequences that actively exploit spatial and temporal structure. 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. 
Maximilian Soelch, Justin Bayer, Marvin Ludersdorfer, Patrick van der   Smagt
stat.ML cs.LG