  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 an 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, parameter-free 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.
