Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series
This addresses anomaly detection for robotics applications, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of anomaly detection in high-dimensional time series, specifically for robot data, by applying a Stochastic Recurrent Network (STORN) and demonstrated robust detection both off- and on-line.
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.