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:2607. 23165v1 Announce Type: cross Abstract: We propose ABF-T-GLCP, a model-agnostic framework for forecasting and uncertainty quantification in nonstationary multivariate time series.
By Ziling Ma, Junshu Jiang, \'Angel L\'opez-Oriona, Ying Sun, Hernando Ombao
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: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
SPACE is a conformal wrapper that creates ellipsoidal joint prediction regions for multivariate time‑series forecasts by estimating time‑local covariance directly from the current forecast sample cloud. It calibrates the region’s radius using a dynamic backward window‑selection scheme, avoiding reliance on historical residuals. Experiments on diverse datasets show that SPACE improves joint and rolling coverage, achieving better coverage‑efficiency tradeoffs than existing wrappers.
By Baishi Li, Kelvin J. L. Koa, Ke-Wei Huang
arXiv:2511. 04275v2 Announce Type: replace-cross Abstract: Conformal prediction has emerged as a powerful framework for constructing distribution-free prediction sets with guaranteed coverage assuming only the exchangeability assumption.
By Jungbin Jun, Ilsang Ohn
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: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
The paper investigates how deep sequence models—such as recurrent neural networks and Transformers—can be integrated into conformal prediction for time series. It explores three methods: conditional quantile regression, conditional quantile function estimation, and localized conformal prediction, providing theoretical asymptotic conditional coverage guarantees for each. Experiments on real-world datasets demonstrate the practical effectiveness of these approaches.
By Junghwan Lee, Jonghyeok Lee, Yao Xie
arXiv:2608.07479v2 Announce Type: replace-cross
Abstract: Conformal prediction has been touted as a more formal, rigorous approach to adding uncertainty to a forecast. The sole objective of this note...
By Peter Cotton
arXiv:2605. 30292v2 Announce Type: replace-cross Abstract: Conformal prediction methods enjoy strong theoretical and empirical predictive inference performance, provided the data is exchangeable and is treated symmetrically during training.
By Hanyang Jiang, Rina Foygel Barber, Ashwin Pananjady, Yao Xie
arXiv:2606. 15950v1 Announce Type: cross Abstract: Conformal prediction gives prediction intervals with finite-sample coverage when the data are exchangeable.
By Jeffery Opoku, David Banahene