Beyond Output-Space Calibration: Spectral Evidence Bundling for Selective Reliability Estimation in Time-Series Classification
This paper introduces a validation-gated reliability estimation method that bundles output confidence with whole-sample spectral descriptors (band energy, entropy, peak dominance, period support, phase stability) to estimate trustworthiness without altering backbone predictions. On eight UCR/UEA datasets and eight backbone families, the method improves Corr-AURC from 0.693 to 0.786 and reduces [email protected] to 0.094.
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[Submitted on 29 Jun 2026]
Title:Beyond Output-Space Calibration: Spectral Evidence Bundling for Selective Reliability Estimation in Time-Series Classification
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Abstract:Post-hoc calibration for time-series classification usually remaps output scores, but deployment decisions such as trust, abstention, and review depend on whether a confident prediction is supported by the current temporal signal. We address three time-series reliability gaps: identical confidence values can hide different temporal support, average calibration can miss false high-confidence errors, and output-space recalibration offers limited input-linked auditability. We introduce a validation-gated fixed-label reliability policy that keeps the backbone prediction unchanged while estimating whether it should be trusted. The method combines output-side cues with whole-sample spectral descriptors, including band energy, entropy, peak dominance, period support, and phase stability, to form a scalar reliability estimate and diagnostic band-level evidence. A validation gate enables spectral conditioning only when correctness ranking improves without breaching [email protected] or AURC tolerances; otherwise it reverts to the safer output-space baseline. Across eight heterogeneous UCR/UEA datasets, eight time-series backbone families, and standard recalibrators, the unconstrained method improves fixed-label selective-reliability metrics on the matched evaluation subset, raising Corr-AURC from 0.693 to 0.779. The validation-gated policy further improves Corr-AURC to 0.786 and reduces [email protected] to 0.094. These results suggest that reliability estimation for time-series classifiers benefits from bundling output confidence with spectral evidence, while validation gating prevents unsupported spectral conditioning.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.18279 [cs.LG]
(or arXiv:2607.18279v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.18279
arXiv-issued DOI via DataCite
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From: Filippo Cenacchi [view email] [v1] Mon, 29 Jun 2026 01:31:00 UTC (1,105 KB)
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