Artificial Intelligence · 14.08.2026, 10:25 UTC
From Recoverability to Functional Use: Certifying Temporal Reports in Time-Series Forecasting
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | arXiv cs.LG ↗ |
| Veröffentlicht | 14.08.2026 UTC |
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arXiv:2608.10433v2 Announce Type: replace Abstract: Temporal reports are increasingly emitted alongside numerical forecasts and are often interpreted as statements about the computation producing those forecasts. We formalize the resulting certification problem as three distinct stages: \emph{recoverability}, \emph{report correctness}, and \emph{functional use}. For point delays, an exact finite-sample recovery--substitutability identity ties structural discrimination and proxy prediction to the same realized shift geometry while placing them on different scales: structural evidence grows with $n\eta_n$, whereas the normalized predictive penalty is governed by $\eta_n$. A delay can therefore be statistically decisive while an alternative lag remains near-oracle. Guided by this regime, we evaluate TCN- and N-HiTS-based systems on the strict intersection of recoverable trajectories, correct reports, and near-oracle one-step predictions. Their dominant forecast dependence remains far from the reported delay under masking, finite perturbations, local Jacobians, and in-distribution conditional replacement, and the separation persists across a 100-fold forecast-loss sweep. A no-bypass factorization then provides an explicit access certificate for the final stage, while architecture-matched multi-seed, post-hoc, and gate-destruction controls identify report-coordinate access as the mechanism governing alignment. The resulting framework separates statistical evidence for \emph{what can be identified} from computational evidence for \emph{what the forecast uses}.