Towards Explainable Sequential Learning

• URL: http://arxiv.org/abs/2505.23624v1
• Date: Thu, 29 May 2025 16:30:59 GMT
• Title: Towards Explainable Sequential Learning
• Authors: Giacomo Bergami, Emma Packer, Kirsty Scott, Silvia Del Din,
• Abstract summary: This paper offers a hybrid explainable temporal data processing pipeline, DataFul Explainable MultivariatE coRrelatIonal Temporal Artificial inTElligence (EMeriTAte+DF)
• Score: 0.2318095974878009
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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