Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data

Chen Xu; Yao Xie

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arXiv · 2105.11886

Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data

Chen Xu·Yao Xie

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Abstract

We develop a distribution-free, unsupervised anomaly detection method called ECAD, which wraps around any regression algorithm and sequentially detects anomalies. Rooted in conformal prediction, ECAD does not require data exchangeability but approximately controls the Type-I error when data are normal. Computationally, it involves no data-splitting and efficiently trains ensemble predictors to increase statistical power. We demonstrate the superior performance of ECAD on detecting anomalous spatio-temporal traffic flow.

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Chen Xu, Yao Xie. 2021-05-25. Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data. https://arxiv.org/abs/2105.11886

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