ECATS: Explainable-by-design concept-based anomaly detection for time series
<|reference_start|>ECATS: Explainable-by-design concept-based anomaly detection for time series: 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.<|reference_end|>
@article{ferfoglia2024ecats:,
    title={ECATS: Explainable-by-design concept-based anomaly detection for time
  series},
    author={Irene Ferfoglia, Gaia Saveri, Laura Nenzi, Luca Bortolussi},
    journal={arXiv preprint arXiv:2405.10608},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10608},
    primaryClass={cs.LG cs.AI}
}