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arXiv · 2505.23624

Towards Explainable Sequential Learning

Giacomo Bergami·Emma Packer·Kirsty Scott·Silvia Del Din
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Abstract

This paper offers a hybrid explainable temporal data processing pipeline, DataFul Explainable MultivariatE coRrelatIonal Temporal Artificial inTElligence (EMeriTAte+DF), bridging numerical-driven temporal data classification with an event-based one through verified artificial intelligence principles, enabling human-explainable results. This was possible through a preliminary a posteriori explainable phase describing the numerical input data in terms of concurrent constituents with numerical payloads. This further required extending the event-based literature to design specification mining algorithms supporting concurrent constituents. Our previous and current solutions outperform state-of-the-art solutions for multivariate time series classifications, thus showcasing the effectiveness of the proposed methodology.

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Giacomo Bergami, Emma Packer, Kirsty Scott, Silvia Del Din. 2025-05-29. Towards Explainable Sequential Learning. https://arxiv.org/abs/2505.23624

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