GRAIL: From Saliency to Semantics in Time-Series Explanation

Authors: Enver Menadjiev , Yiqiu Shen , Jaesik Choi , Julia Stoyanovich

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Abstract: Explaining a time-series model requires both locating the evidence and extracting meaningful knowledge from it. Yet a single timestep is not a semantic unit: the meaningful patterns live across many samples, not in any one of them. Per-timestep saliency is therefore noisy, high-dimensional, and unstable. Existing methods address this by introducing fixed-size windows and treating them as independent players in a cooperative game. Evaluating a coalition of such players requires defining a ``baseline'' region, which prior work tackles with fixed counterfactuals (e.g., a flat line, noise, or a spectral surrogate). This choice frequently dominates the resulting explanation, obscuring the model's true decisions.

To address these issues, we propose GRAIL, a region-based time-series explanation framework that uses domain-specific intervals, such as the P-wave and the QRS complex of an electrocardiogram (ECG) beat, as players. We describe each region with human-readable features and generate counterfactual baselines with an on-manifold conditional generator. Such a choice of players also admits non-temporal features: static covariates enter as singleton players attributed with the temporal regions on one scale, so a patient's tabular evidence (e.g., age, sex) is weighed directly against the signal. We attribute the predictions to the features via Owen values with common-random-number variance reduction. Experimental results on both synthetic and real data show that the proposed framework produces faithful and robust explanations, evaluated under matched on- and off-manifold operators, offering meaningful knowledge to domain experts.

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.29036 [cs.AI]

(or

arXiv:2610.29036v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.29036

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arXiv-issued DOI via DataCite (pending registration)

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From: Enver Menadjiev [ view email ]

[v1]

Wed, 21 Oct 2026 01:09:56 UTC (149 KB)

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