SCAN improves time series anomaly detection by integrating multi-scale clustering to guide reconstruction toward normal patterns and supply a dual anomaly criterion.
A comparative study on unsupervised anomaly detection for time series: Experiments and analysis,
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Active learning with masked reconstruction and minimax training raises AUC by 12.39% across 28 test cases on four multivariate datasets and seven unsupervised backbones.
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SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering
SCAN improves time series anomaly detection by integrating multi-scale clustering to guide reconstruction toward normal patterns and supply a dual anomaly criterion.
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Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning
Active learning with masked reconstruction and minimax training raises AUC by 12.39% across 28 test cases on four multivariate datasets and seven unsupervised backbones.