Autonomous anomaly detection for streaming data

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Autonomous anomaly detection for streaming data

Anomaly detection from data streams is a hotly studied topic in the machine learning domain. It is widely considered a challenging task because the underlying patterns exhibited by the streaming data may dynamically change at any time. In this paper, a new algorithm is proposed to detect anomalies a...

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Bibliographic Details
Published in:	KNOWLEDGE-BASED SYSTEMS
Main Authors:	Basheer, Muhammad Yunus Iqbal; Ali, Azliza Mohd; Hamid, Nurzeatul Hamimah Abdul; Ariffin, Muhammad Azizi Mohd; Osman, Rozianawaty; Nordin, Sharifalillah; Gu, Xiaowei
Format:	Article; Early Access
Language:	English
Published:	ELSEVIER 2024
Subjects:	Computer Science
Online Access:	https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001133574000001

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