COPOD: Copula-Based Outlier Detection

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...

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Bibliographic Details
Published in2020 IEEE International Conference on Data Mining (ICDM) pp. 1118 - 1123
Main Authors Li, Zheng, Zhao, Yue, Botta, Nicola, Ionescu, Cezar, Hu, Xiyang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2020
Subjects
Anomaly detection
Computational efficiency
Computational modeling
Conferences
copula
data mining
Data models
outlier detection
Prediction algorithms
Task analysis
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Summary: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.
ISSN:2374-8486
DOI:10.1109/ICDM50108.2020.00135