INSHAPE discovers instance-specific non-overlapping shapelets, models their temporal dependencies, and aggregates them bottom-up into population-level prototypes for improved accuracy and interpretability in time-series classification.
Early prediction of circulatory failure in the intensive care unit using machine learning.Nature medicine, 26(3):364–373,
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INSHAPE: Instance-Level Shapelets for Interpretable Time-Series Classification
INSHAPE discovers instance-specific non-overlapping shapelets, models their temporal dependencies, and aggregates them bottom-up into population-level prototypes for improved accuracy and interpretability in time-series classification.