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Abstract

Outlier detection refers to the identification of rare
items that are deviant from the general data distribution. Existing
approaches suffer from high computational complexity, low
predictive capability, and limited interpretability. As a remedy, we
present a novel outlier detection algorithm called COPOD, which
is inspired by copulas for modeling multivariate data distribution.
COPOD first constructs a empirical copula, and then uses it to
predict tail probabilities of each given data point to determine its
level of “extremeness”. Intuitively, we think of this as calculating
an anomalous p-value. This makes COPOD both parameter-free,
highly interpretable, and computationally efficient. In this work,
we make three key contributions, 1) propose a novel, parameterfree
outlier detection algorithm with both great performance
and interpretability, 2) perform extensive experiments on 30
benchmark datasets to show that COPOD outperforms in most
cases and is also one of the fastest algorithms, and 3) release an
easy-to-use Python implementation for reproducibility.