Dynamic Regime-Aware Conformal Prediction (DRACP) is a new method that blends density‑ratio estimation, localized kernel weighting, and probabilistic regime‑aware weighting with a self‑tuning online significance controller to produce reliable prediction intervals under multiple distribution shifts. The authors prove finite‑sample validity with oracle weights, provide a coverage‑gap bound for estimated weights, and give deterministic or regret guarantees for the online controller. In experiments on 48 real forecasting series—including euro‑area inflation, US macroeconomic and energy indicators, and daily financial data—DRACP achieves the most reliable calibration, maintaining coverage close to the nominal 0.90 and never falling below 0.80, while other methods achieve narrower intervals but with higher under‑coverage.
whyItMatters":"DRACP offers a principled trade‑off between calibration and efficiency, ensuring that prediction intervals meet coverage standards even when economic data exhibit covariate shift, concept drift, and latent regimes."
By Bogdan Oancea
arXiv:2606. 31915v1 Announce Type: cross Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost.
By Jiachen Cong, Jingbo Liu
arXiv:2606. 16356v1 Announce Type: new Abstract: We study uncertainty quantification for aggregated forecasting tasks such as annual totals and year-over-year growth rates.
By Andro Sabashvili
arXiv:2608. 10553v1 Announce Type: cross Abstract: Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters, but its standard calibration procedure is often inefficient for time series data, where forecast errors are temporally dependent and change across time and operating conditions.
By Sangjin Jin, Kangmin Kim, Junhyeong Lee, Yongjae Lee
Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters, but its standard calibration procedure is often inefficient for time series data, where forecast errors are temporally dependent and change across time and operating conditions. Recent time series CP methods improve local calibration using recent, weighted, or localized residuals.
arXiv:2508.10336v3 Announce Type: replace-cross
Abstract: In a supervised online setting, quantifying uncertainty has been proposed in the seminal work of Gibbs and Cand\`es (2021). For any given poi...
By Pierre Humbert, Ulysse Gazin, Ruth Heller, Etienne Roquain