Structure-Aware Graph Abstention for Reliable Selective Forecasting

Abstract:Selective forecasting abstains on high-risk test windows under a retained-coverage budget. Existing gates such as TEM (Brusokas et al., 2025) score each forecast as a whole; for multivariate outputs, trajectories can look plausible while violating dependencies among variables. We treat instance-level plausibility and relational consistency as distinct reliability axes and operationalize the latter via a learned sparse graph and a Dirichlet-style structural energy E_struct, trained with error-weighted graph regularization and score-error alignment. On seven long-horizon benchmarks and four backbones, structural gating often reduces selective MSE versus TEM at matched coverage, with the largest gains where cross-variable structure appears more informative in our benchmarks; gains are not universal, indicating a complementary abstention signal. Table 1 is a Protocol A ranking diagnostic (seed 2024); three-seed deployable Protocol B on an aligned subset is in Table 3 (full validation-to-test grids: Appendix A).

Machine Learning (cs.LG)

arXiv:2610.08322 [cs.LG]

(or arXiv:2610.08322v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.08322 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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