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Towards Explainable Sequential Learning

Giacomo Bergami¹, Emma Packer², Kirsty Scott² and Silvia Del Din^{2, 3}

1. School of Computing, Newcastle University
   giacomo.bergami@newcastle.ac.uk
2. Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University
   {e.packer,kirsty.scott-singer}@newcastle.ac.uk
3. National Institute for Health and Care Research (NIHR), Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust
   silvia.del-din@newcastle.ac.uk

Abstract

This paper offers a hybridly 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 humanexplainable 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 algorithms for multivariate time series classifications over four dataset considered in the present paper, thus showcasing the effectiveness of the proposed methodology premiering the extraction of explainable correlations across Multivariate Time Series (MTS) dimensions with dataful features.

Key words

Verified AI; eXplainable AI (XAI); polyadic logs; Data Trends; Poly-DECLARE; Multivariate Time Series Classification

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS250303077B

Publication information

Volume 23, Issue 1 (January 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

Available in PDF
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How to cite

Bergami, G., Packer, E., Scott, K., Din, S. D.: Towards Explainable Sequential Learning. Computer Science and Information Systems, Vol. 23, No. 1, 443-473. (2026), https://doi.org/10.2298/CSIS250303077B