arXiv:2608. 17333v1 Announce Type: cross Abstract: Modern probabilistic time-series forecasters often express uncertainty through forecast samples.
arXiv:2508. 13362v2 Announce Type: replace Abstract: Conformal prediction (CP) is well-suited for uncertainty quantification in time series forecasting due to its distribution-free coverage guarantees.
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.
arXiv:2608. 17079v1 Announce Type: new Abstract: Conformal prediction provides distribution-free prediction intervals but relies on exchangeability, an assumption often violated in economic forecasting because of covariate shift, concept drift, local heterogeneity and latent regimes.
arXiv:2606. 09473v1 Announce Type: cross Abstract: Probabilistic forecasters are increasingly learned, yet the baselines they are compared against are often weak or omitted.
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.
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. 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.
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.
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.
arXiv:2507. 20975v5 Announce Type: replace-cross Abstract: Operator models are regression algorithms between Banach spaces of functions.
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.
arXiv:2606. 05264v1 Announce Type: new Abstract: Training robust multivariate time series forecasting models requires large, diverse corpora, yet many real-world domains provide only a handful of observed sequences.