  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.
