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arXiv · 1602.07109

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

Maximilian Soelch·Justin Bayer·Marvin Ludersdorfer·Patrick van der Smagt
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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 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.

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Maximilian Soelch, Justin Bayer, Marvin Ludersdorfer, Patrick van der Smagt. 2016-06-14. Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series. https://arxiv.org/abs/1602.07109

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