GRAIL: From Saliency to Semantics in Time-Series Explanation

GRAIL is a region‑based framework for explaining time‑series models that moves beyond per‑timestep saliency by treating domain‑specific intervals—such as the P‑wave and QRS complex in ECG data—as players in a cooperative game. It describes each region with human‑readable features, generates counterfactual baselines using an on‑manifold conditional generator, and incorporates static covariates as singleton players to directly weigh tabular evidence against the signal. The method attributes predictions via Owen values with variance reduction, and experiments on synthetic and real data demonstrate faithful, robust explanations that provide meaningful knowledge to domain experts.

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