Time-series anomaly detection in real-world streams is often challenged by evolving operating conditions, where distribution shifts can be easily mistaken for anomalies. Due to this, we study a new problem, online latent-domain anomaly detection, where domain labels and shift times are unobserved, the number of domains...
Yi-Meng Lu, Yi-Fei Gao, Tian Lan et al.· Proceedings of the 32nd ACM...· 0 citations
Time series anomaly detection (TSAD) remains challenging not only because anomaly labels are scarce, but also because temporal anomalies are highly context-dependent. Existing methods often rely on unsupervised objectives or surrogate abnormal patterns, providing limited supervision for context-dependent normal--anomal...
Yi-Fei Gao, Tian Lan, Yi-Meng Lu et al.· 0 citations
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