Towards explainable sequential learning

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.