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.

翻译：我们开发了一种无分配、不受监督的异常现象探测方法,叫做ECAD,它环绕着任何回归算法,并依次探测异常现象。 ECAD基于符合逻辑的预测,并不要求数据互换性,而是在数据正常时大致控制了I型错误。 计算上,它不涉及数据分割和高效列车混合预测器来增加统计力量。 我们展示了ECAD在发现异常时流量方面的优异表现。