%0 Journal Article %1 solch2016variational %A Sölch, Maximilian %A Bayer, Justin %A Ludersdorfer, Marvin %A van der Smagt, Patrick %D 2016 %K %T Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series %U http://arxiv.org/abs/1602.07109 %X 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.

@article{solch2016variational, 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.}, added-at = {2023-12-12T19:55:38.000+0100}, author = {Sölch, Maximilian and Bayer, Justin and Ludersdorfer, Marvin and van der Smagt, Patrick}, biburl = {https://www.bibsonomy.org/bibtex/2fe647f987a9d4842031ee2bf034a9422/admin}, interhash = {27fdf28483e7ea154c116b58b4492fa0}, intrahash = {fe647f987a9d4842031ee2bf034a9422}, keywords = {}, note = {cite arxiv:1602.07109}, timestamp = {2023-12-12T19:55:38.000+0100}, title = {Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series}, url = {http://arxiv.org/abs/1602.07109}, year = 2016 }