Prototypes as Explanation for Time Series Anomaly Detection

TL;DR

This paper introduces ProtoAD, a prototype-based explanation method for time series anomaly detection that enhances interpretability without sacrificing detection accuracy.

Contribution

It extends prototype learning to anomaly detection, providing visual and intuitive explanations for model decisions in time series analysis.

Findings

Prototypes improve interpretability of anomaly detection models.

Visualizations help understand regular and abnormal patterns.

Method maintains detection performance while enhancing transparency.

Abstract

Detecting abnormal patterns that deviate from a certain regular repeating pattern in time series is essential in many big data applications. However, the lack of labels, the dynamic nature of time series data, and unforeseeable abnormal behaviors make the detection process challenging. Despite the success of recent deep anomaly detection approaches, the mystical mechanisms in such black-box models have become a new challenge in safety-critical applications. The lack of model transparency and prediction reliability hinders further breakthroughs in such domains. This paper proposes ProtoAD, using prototypes as the example-based explanation for the state of regular patterns during anomaly detection. Without significant impact on the detection performance, prototypes shed light on the deep black-box models and provide intuitive understanding for domain experts and stakeholders. We extend the widely used prototype learning in classification problems into anomaly detection. By visualizing both the latent space and input space prototypes, we intuitively demonstrate how regular data are modeled and why specific patterns are considered abnormal.