TempShap: When and How Much a Feature Matters in Time-Series Forecasting

Authors: Chi Zhao , Anastasiia Chelnokova , Jing Liu , Elena Parilina

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Abstract: Attribution for a time-series forecaster should answer two questions that a feature-level scalar cannot: when in the input window a feature acts, and how much its influence changes across the forecast horizon. Most existing Shapley explainers treat each feature as one player and therefore collapse both axes. We propose TempShap, a retrospective, loss-based Shapley framework whose players are the individual input cells $(t,i)$. Its attribution tensor yields temporal-localization and horizon-impact profiles. We validate these against planted temporal structures, a tractable exact cell game, and a closed-form attribution oracle for a real linear forecaster. Permutation sharing estimates the cell-level game at localized perturbation cost. Under redundant inputs, one-at-a-time scores under-credit substitutable information, whereas the signed Shapley decomposition preserves total value and yields more reliable held-out deletion rankings under mean masking. A planted counterexample isolates the redundancy mechanism. Experiments show a deletion-ranking advantage across Shapley estimators, forecasting benchmarks, and model families. TempShap therefore makes fine-grained Shapley attribution both practical and operationally useful.

Subjects:

Artificial Intelligence (cs.AI) ; Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)

MSC classes:

68T05, 68T01, 91A12, 62M10

ACM classes:

I.2.0; I.2.6; G.3

Cite as:

arXiv:2610.33330 [cs.AI]

(or

arXiv:2610.33330v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.33330

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Chi Zhao [ view email ]

[v1]

Fri, 23 Oct 2026 22:08:16 UTC (250 KB)

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