Extracting Explainable Temporal Features in Multivariate Time Series Classification Pipelines

Multivariate Time Series Classification (MTSC) is a central task in modern data analytics, with growing impact across domains such as healthcare, finance, and industrial monitoring. As MTSC models are increasingly used in real-world decision-making, the need for explainability has become critical. Existing solutions either rely on feature-extraction frameworks that produce opaque descriptors or on explainable-by-design models tied to specific architectures.