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

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

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 The model trained on old-distribution data becomes outdated and ineffective after such changes.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...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/9757
https://ink.library.smu.edu.sg/context/sis_research/article/10757/viewcontent/2310.05668v4.pdf

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