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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.<\/jats:p>","DOI":"10.2298\/csis250303077b","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T14:17:48Z","timestamp":1764944268000},"page":"443-473","source":"Crossref","is-referenced-by-count":0,"title":["Towards explainable sequential learning"],"prefix":"10.2298","volume":"23","author":[{"given":"Giacomo","family":"Bergami","sequence":"first","affiliation":[{"name":"School of Computing, Newcastle University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emma","family":"Packer","sequence":"additional","affiliation":[{"name":"Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kirsty","family":"Scott","sequence":"additional","affiliation":[{"name":"Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Silvia","family":"Del-Din","sequence":"additional","affiliation":[{"name":"Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University + National Institute for Health and Care Research (NIHR), Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"van der Aalst, W.M.P.: Discovering Directly-Follows Complete Petri Nets from Event Data, pp. 539-558. 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