Feb. 15, 2024, 5:43 a.m. | Thomas Lai, Thi Kieu Khanh Ho, Narges Armanfard

cs.LG updates on arXiv.org arxiv.org

arXiv:2310.12294v2 Announce Type: replace
Abstract: Numerous methods for time series anomaly detection (TSAD) methods have emerged in recent years. Most existing methods are unsupervised and assume the availability of normal training samples only, while few supervised methods have shown superior performance by incorporating labeled anomalous samples in the training phase. However, certain anomaly types are inherently challenging for unsupervised methods to differentiate from normal data, while supervised methods are constrained to detecting anomalies resembling those present during training, failing to …

abstract anomaly anomaly detection arxiv availability cs.lg detection multivariate normal performance samples series set time series training type types unsupervised

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