Self-supervised spatial-temporal normality learning for time series anomaly detection

• Main Authors: CHEN, Yutong, XU, Hongzuo, PANG, Guansong, QIAO, Hezhe, ZHOU, Yuan, SHANG, Mingsheng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Anomaly Detection; Time Series; Self-supervised Learning; Normality Learning; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9874; https://ink.library.smu.edu.sg/context/sis_research/article/10874/viewcontent/2406.19770v1.pdf
• Institution: Singapore Management University

Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is to learn the normal patterns of time series data, thereby identifying deviations in test samples. Most existing TSAD me...