The Failures of Marginal Influence-Based Attribution Methods for Global Time Series Explanations

Abstract:Explainability methods for time series models predominantly produce flat attribution scores: they quantify the direct influence of a feature at a timestamp by a scalar. We prove that the dominant failure mode of such methods is not the scalar format itself but a fundamental computational mismatch: existing methods compute scores via marginal conditioning or off-manifold gradients, both of which conflate direct temporal dependencies with mediated ones under autocorrelation. We also define DAG-faithfulness: an explanation is DAG-faithful if the temporal dependency graph it encodes is Markov-equivalent to the temporal directed acyclic graph (DAG) implicitly learned by the model. Particularly, we observe that standard attribution methods, specifically SHAP, are not DAG-faithful in general, and that recent time-series-aware extensions inherit the same computational limitation.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.16236 [cs.LG]

(or arXiv:2607.16236v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.16236 Focus to learn more arXiv-issued DOI via DataCite

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