  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.
