Prototypes as Explanation for Time Series Anomaly Detection
Bin Li Carsten Jentsch Emmanuel Müller
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.