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Book Chapter

COPOD: Copula-Based Outlier Detection

Authors

Li,  Zheng
External Organizations;

Zhao,  Yue
External Organizations;

/persons/resource/nicola.botta

Botta,  Nicola
Potsdam Institute for Climate Impact Research;

Ionescu,  Cezar
Potsdam Institute for Climate Impact Research;

Hu,  Xiyang
External Organizations;

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Citation

Li, Z., Zhao, Y., Botta, N., Ionescu, C., Hu, X. (in press): COPOD: Copula-Based Outlier Detection. - In: IEEE International Conference on Data Mining (ICDM), IEEE.


Cite as: https://publications.pik-potsdam.de/pubman/item/item_24536
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.