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We develop a distribution-free, unsupervised anomaly detection method called ECAD, which wraps around any regression algorithm and sequentially detects anomalies.
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“Conformalized quantile regression”
Yaniv Romano, Evan Patterson and Emmanuel Candes · 2019
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“Deep Learning for Anomaly Detection: A Survey”, 2019
Raghavendra of Sydney, Capital Centre, Sanjay Institute and Hbku · 2019
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“PyOD: A Python Toolbox for Scalable Outlier Detection”
Yue Zhao, Zain Nasrullah and Zheng Li · 2019
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A. Angelopoulos, Stephen Bates, J. Malik and Michael. Jordan · 2020
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“No free lunch but a cheaper supper: A general framework for streaming anomaly detection”
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“Contextual Online False Discovery Rate Control”
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