  Diffusion models have been recently used for anomaly detection (AD) in
images. In this paper we investigate whether they can also be leveraged for AD
on multivariate time series (MTS). We test two diffusion-based models and
compare them to several strong neural baselines. We also extend the PA%K
protocol, by computing a ROCK-AUC metric, which is agnostic to both the
detection threshold and the ratio K of correctly detected points. Our models
outperform the baselines on synthetic datasets and are competitive on
real-world datasets, illustrating the potential of diffusion-based methods for
AD in multivariate time series.
