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This document reviews various techniques for time-series anomaly detection, highlighting the challenges of high false-positive rates and the absence of a universally effective anomaly detector. It categorizes anomalies into point, contextual, and collective types, and discusses the importance of data types and contexts in detection methods. The paper also explores machine learning approaches, including supervised, semi-supervised, and unsupervised techniques, while emphasizing the need for effective computa

Author

Truong Son Pham

Language

EN