COPOD: Copula-Based Outlier Detection

TL;DR

COPOD is a novel, parameter-free outlier detection method based on copulas that offers high interpretability, efficiency, and strong performance across diverse datasets.

Contribution

The paper introduces COPOD, a new copula-based outlier detection algorithm that is parameter-free, interpretable, and computationally efficient, with extensive experimental validation.

Findings

COPOD outperforms existing methods on most benchmark datasets.

COPOD is one of the fastest outlier detection algorithms.

The method provides highly interpretable outlier scores.

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 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.