ECATS: Explainable-by-design concept-based anomaly detection for time series
This addresses the challenge of interpretability in complex CPS for anomaly detection, but it appears incremental as it builds on existing concept-based and STL methods.
The authors tackled the problem of explainability in deep learning for time series anomaly detection in Cyber Physical Systems by proposing ECATS, a concept-based neuro-symbolic architecture using Signal Temporal Logic, which achieved great classification performance in preliminary experiments.
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 a simple CPS-based dataset show that our model is able to achieve great classification performance while ensuring local interpretability.