Spectral Adaptive Conformal Prediction for Structured Non-Exchangeable Data
Conformal prediction gives prediction intervals with finite-sample coverage when the data are exchangeable. Many time-indexed datasets are not exchangeable: they have seasons, recurring regimes, changing frequencies, or other forms of structured dependence. This paper studies a simple way to use that structure. We propose spectral adaptive conformal prediction, a method that forms weighted conformal quantiles using local spectral similarity and then updates the target miscoverage level online. The spectral weights choose calibration residuals that look relevant to the current test point. The adaptive update corrects the long-run miss rate when uncertainty changes over time. The theory mak...
| Authors | Jeffery Opoku, David Banahene |
|---|---|
| Primary category | stat.ML |
| Published | 14 June 2026 |
| Last updated at source | 18 July 2026 |
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