MadSGM: Multivariate Anomaly Detection with Score-based Generative
  Models
Haksoo Lim Sewon Park Minjung Kim Jaehoon Lee Seonkyu Lim Noseong Park
The time-series anomaly detection is one of the most fundamental tasks for
time-series. Unlike the time-series forecasting and classification, the
time-series anomaly detection typically requires unsupervised (or
self-supervised) training since collecting and labeling anomalous observations
are difficult. In addition, most existing methods resort to limited forms of
anomaly measurements and therefore, it is not clear whether they are optimal in
all circumstances. To this end, we present a multivariate time-series anomaly
detector based on score-based generative models, called MadSGM, which considers
the broadest ever set of anomaly measurement factors: i) reconstruction-based,
ii) density-based, and iii) gradient-based anomaly measurements. We also design
a conditional score network and its denoising score matching loss for the
time-series anomaly detection. Experiments on five real-world benchmark
datasets illustrate that MadSGM achieves the most robust and accurate
predictions.