LARA : A light and anti-overfitting retraining approach for unsupervised time series anomaly detection

• Main Authors: CHEN, Feiyi, QIN, Zhen, ZHOU, Mengchu, ZHANG, Yingying, DENG, Shuiguang, FAN, Lunting, PANG, Guansong, WEN, Qingsong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2025
• Subjects: Anomaly detection; Time series; Light overhead; Anti-overfitting; Web log analysis; Artificial Intelligence and Robotics; Digital Communications and Networking
• Online Access: https://ink.library.smu.edu.sg/sis_research/9757; https://ink.library.smu.edu.sg/context/sis_research/article/10757/viewcontent/2310.05668v4.pdf
• Institution: Singapore Management University

Most of current anomaly detection models assume that the normal pattern remains the same all the time. However, the normal patterns of web services can change dramatically and frequently over time. The model trained on old-distribution data becomes outdated and ineffective after such changes. Retrai...