Unraveling the ‘anomaly’ in time series anomaly detection: A self-supervised tri-domain solution

• Main Authors: SUN, Yuting, PANG, Guansong, YE, Guanhua, CHEN, Tong, HU, Xia, YIN, Hongzhi
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Time series; Anomaly detection; Self-supervised learning; Contrastive learning; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9282; https://ink.library.smu.edu.sg/context/sis_research/article/10282/viewcontent/2311.11235v2.pdf
• Institution: Singapore Management University

The ongoing challenges in time series anomaly detection (TSAD), including the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more robust and efficient solution. As limited anomaly labels hinder traditional supervised models in anomaly detecti...