# Explainable Contextual Anomaly Detection using Quantile Regression Forests

arXiv: https://arxiv.org/abs/2302.11239 · HTML: https://mlsanity.com/paper/2302.11239/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 72 references that are in the map.
- First posted on arXiv 22 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [A Survey on Explainable Anomaly Detection](https://mlsanity.com/paper/2210.06959/) (2022)
- [Attribution-based Explanations that Provide Recourse Cannot be Robust](https://mlsanity.com/paper/2205.15834/) (2022)
- [Contextual Outlier Interpretation](https://mlsanity.com/paper/1711.10589/) (2017)
- [Robust Contextual Outlier Detection: Where Context Meets Sparsity](https://mlsanity.com/paper/1607.08329/) (2016)
- [Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection](https://mlsanity.com/paper/2101.11560/) (2021)
- [Detecting Outliers in Data with Correlated Measures](https://mlsanity.com/paper/1808.08640/) (2018)
- [Learning Representations for Outlier Detection on a Budget](https://mlsanity.com/paper/1507.08104/) (2015)

## Built on by (most cited first, 10 of 10)

- [Context-aware Video Anomaly Detection in Long-Term Datasets](https://mlsanity.com/paper/2404.07887/) (2024)
- [Graph Neural Networks based Log Anomaly Detection and Explanation](https://mlsanity.com/paper/2307.00527/) (2023)
- [A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures](https://mlsanity.com/paper/2404.00673/) (2024)
- [uLEAD-TabPFN: Uncertainty-aware Dependency-based Anomaly Detection with TabPFN](https://mlsanity.com/paper/2604.20255/) (2026)
- [Latent Anomaly Detection Through Density Matrices](https://mlsanity.com/paper/2408.07623/) (2024)
- [Monotonic anomaly detection](https://mlsanity.com/paper/2410.23158/) (2024)
- [Detecting Anomalies Using Rotated Isolation Forest](https://mlsanity.com/paper/2501.17787/) (2025)
- [Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)](https://mlsanity.com/paper/2501.18417/) (2025)
- [Dealing with Uncertainty in Contextual Anomaly Detection](https://mlsanity.com/paper/2507.04490/) (2025)
- [Rarity-Gated Context Conditioning for Offline Imitation Learning-Based Maritime Anomaly Detection](https://mlsanity.com/paper/2606.13311/) (2026)

## Abstract (arXiv)

Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection methods aim to detect objects that deviate from other objects within a context of similar objects by dividing the features into contextual features and behavioral features. In this paper, we develop connections between dependency-based traditional anomaly detection methods and contextual anomaly detection methods. Based on resulting insights, we propose a novel approach to inherently interpretable contextual anomaly detection that uses Quantile Regression Forests to model dependencies between features. Extensive experiments on various synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art anomaly detection methods in identifying contextual anomalies in terms of accuracy and interpretability.