TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data
<|reference_start|>TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data: As attention to recorded data grows in the realm of automotive testing and manual evaluation reaches its limits, there is a growing need for automatic online anomaly detection. This real-world data is complex in many ways and requires the modelling of testee behaviour. To address this, we propose a temporal variational autoencoder (TeVAE) that can detect anomalies with minimal false positives when trained on unlabelled data. Our approach also avoids the bypass phenomenon and introduces a new method to remap individual windows to a continuous time series. Furthermore, we propose metrics to evaluate the detection delay and root-cause capability of our approach and present results from experiments on a real-world industrial data set. When properly configured, TeVAE flags anomalies only 6% of the time wrongly and detects 65% of anomalies present. It also has the potential to perform well with a smaller training and validation subset but requires a more sophisticated threshold estimation method.<|reference_end|>
@article{correia2024tevae:,
    title={TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly
  Detection in Variable-state Multivariate Time-series Data},
    author={Lucas Correia, Jan-Christoph Goos, Philipp Klein, Thomas B"ack, Anna
  V. Kononova},
    journal={arXiv preprint arXiv:2407.06849},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06849},
    primaryClass={cs.LG cs.AI cs.CE}
}