Abstract
Anomaly detection in multivariate time series (MTS) from sensor data is critical in many industrial applications. The challenge lies in managing massive unlabeled datasets with complex spatio-temporal correlations, diverse anomalies, and noise. While several unsupervised methods have been proposed, they are often limited to specific applications. In this paper, we introduce a probabilistic self-supervised framework, Autoregressive Density Estimation Transformer (ADET). ADET integrates an efficient transformer for learning spatio-temporal representations with density estimation networks for multi-score anomaly detection, focusing on point-to-point, point-to-distribution, and distribution-to-distribution distances. ADET improves noise resilience using optimal truncated singular value decomposition (OT-SVD) in an end-to-end optimization process. We conducted experiments by employing several encoders and performed an ablation study to examine the effect of OT-SVD.