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Towards Explainable Sequential Learning.

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Title: Towards Explainable Sequential Learning.
Authors: Bergami, Giacomo1 giacomo.bergami@newcastle.ac.uk, Packer, Emma2 e.packer@newcastle.ac.uk, Scott, Kirsty2 kirsty.scott-singer@newcastle.ac.uk, Del Din, Silvia2,3 silvia.del-din@newcastle.ac.uk
Source: Computer Science & Information Systems. Jan2026, Vol. 23 Issue 1, p443-473. 31p.
Subjects: Sequential learning, Time series analysis, Data analytics, Artificial intelligence, Classification algorithms, Temporal databases
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. [ABSTRACT FROM AUTHOR]
Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Bergami%2C+Giacomo%22">Bergami, Giacomo</searchLink><relatesTo>1</relatesTo><i> giacomo.bergami@newcastle.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Packer%2C+Emma%22">Packer, Emma</searchLink><relatesTo>2</relatesTo><i> e.packer@newcastle.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Scott%2C+Kirsty%22">Scott, Kirsty</searchLink><relatesTo>2</relatesTo><i> kirsty.scott-singer@newcastle.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Del+Din%2C+Silvia%22">Del Din, Silvia</searchLink><relatesTo>2,3</relatesTo><i> silvia.del-din@newcastle.ac.uk</i>
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  Data: <searchLink fieldCode="JN" term="%22Computer+Science+%26+Information+Systems%22">Computer Science & Information Systems</searchLink>. Jan2026, Vol. 23 Issue 1, p443-473. 31p.
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  Data: <searchLink fieldCode="DE" term="%22Sequential+learning%22">Sequential learning</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Temporal+databases%22">Temporal databases</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.2298/CSIS250303077B
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 31
        StartPage: 443
    Subjects:
      – SubjectFull: Sequential learning
        Type: general
      – SubjectFull: Time series analysis
        Type: general
      – SubjectFull: Data analytics
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Classification algorithms
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      – SubjectFull: Temporal databases
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      – TitleFull: Towards Explainable Sequential Learning.
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              M: 01
              Text: Jan2026
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              Y: 2026
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