HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate Detection of Industrial Multivariate Time Series Anomalies

arXiv:2404.08224v1 Announce Type: cross
Abstract: Multivariate Time Series (MTS) anomaly detection focuses on pinpointing samples that diverge from standard operational patterns, which is crucial for ensuring the safety and security of industrial applications. The primary challenge in this domain is to develop representations capable of discerning anomalies effectively. The prevalent methods for anomaly detection in the literature are predominantly reconstruction-based and predictive in nature. However, they typically concentrate on a single-dimensional instance level, thereby not fully harnessing the complex associations …

anomaly detection applications arxiv challenge consistency cs.ai cs.cr cs.it cs.lg cs.sy detection domain eess.sy hcl industrial math.it mts operational patterns safety security series standard