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

arXiv:2404.08224v1 Announce Type: new
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 …

abstract anomaly anomaly detection applications arxiv challenge cs.ai cs.cr cs.it cs.lg cs.sy detection domain eess.sy hcl hierarchical industrial math.it multivariate patterns safety samples security series standard time series type