ECATS: Explainable-by-Design Concept-Based Anomaly Detection for Time Series

Irene Ferfoglia, Gaia Saveri, Laura Nenzi, Luca Bortolussi

Abstract: Deep learning methods for time series have already reached excellent performances in both prediction and classification tasks, including anomaly detection. However, the complexity inherent in Cyber Physical Systems (CPS) creates a challenge when it comes to explainability methods. To overcome this inherent lack of interpretability, we propose ECATS, a concept-based neuro-symbolic architecture where concepts are represented as Signal Temporal Logic (STL) formulae. Leveraging kernel-based methods for STL, concept embeddings are learnt in an unsupervised manner through a cross-attention mechanism. The network makes class predictions through these concept embeddings, allowing for a meaningful explanation to be naturally extracted for each input. Our preliminary experiments with simple CPS-based datasets show that our model is able to achieve great classification performance while ensuring local interpretability.