COPOD: Copula-Based Outlier Detection

By Zheng Li, Yue Zhao, Nicola Botta, Cezar Ionescu, Xiyang Hu. Industrial Conference on Data Mining, 2020

434 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ba2061d70b2b2ef881bced2fd5898e2bd4ee7a70

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Phillip Wenig
2. Yongjun Wang
3. Yijie Wang
4. Hongzuo Xu
5. Sebastian Schmidl
6. Guansong Pang
7. Thorsten Papenbrock

In Control and Systems Engineering

1. Phillip Wenig
2. Sebastian Schmidl
3. Thorsten Papenbrock

In Statistics and Probability

1. Phillip Wenig
2. Sebastian Schmidl
3. Guansong Pang
4. Thorsten Papenbrock

Citations in the corpus

• Deep Isolation Forest for Anomaly Detection (2022, methodology): “This section aims to further justify this intuition by investigating the effectiveness of combining the randomly initialised network-based representations with other anomaly scoring methods including probability-based anomaly scoring method COPOD [11], density-based method LOF [4], and distance-base”
• Anomaly Detection in Time Series: A Comprehensive Evaluation (2022, background): “…Donut [150] MAD-GAN[77] TAnoGAN [8] PAD[33] Torsk [60] SSA [155] NumentaHTM [3] Telemanom [64] SR-CNN [112] Outlier Detection LOCI/aLOCI[103] MCOD[73] IsolationForest [83] CBLOF [59] BLOF[59] COPOD [80] DBStream [55] LOF [22] DILOF[95] COF [130] GeckoFSM[118] IF-LOF [36] EIF [58] SubsequenceIF [83]”
• Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation (2024, unknown): “…including Mahalanobis distance (Maha) (Hardin and Rocke, 2004; Lee et al., 2018), One-class SVM (SVM) (Schölkopf et al., 2001), Local Outlier Factor (LOF) (Breunig et al., 2000), Kernel Density Estimation (KDE) (Latecki et al., 2007), and Copula-Based Outlier Detection (COPOD) (Li et al., 2020).”