M. Stocker, W. Małgorzewicz, M. Fontana, S. Ben Taieb
arXiv 17 Nov 2025 · Statistics — Methodology
arXiv:2511.13608 · PDF · DOI · OpenAlex · Extracted main text
Conformal prediction is a powerful post-hoc framework for uncertainty quantification that provides distribution-free coverage guarantees. However, these guarantees crucially rely on the assumption of exchangeability. This assumption is fundamentally violated in time series data, where temporal dependence and distributional shifts are pervasive. As a result, classical split-conformal methods may yield prediction intervals that fail to maintain nominal validity. This review unifies recent advances in conformal forecasting methods specifically designed to address nonexchangeable data. We first present a theoretical foundation, deriving finite-sample guarantees for split-conformal prediction under mild weak-dependence conditions. We then survey and classify state-of-the-art approaches that mitigate serial dependence by reweighting calibration data, dynamically updating residual distributions, or adaptively tuning target coverage levels in real time. Finally, we present a comprehensive simulation study that compares these techniques in terms of empirical coverage, interval width, and computational cost, highlighting practical trade-offs and open research directions.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Oliveira, Roberto I. and Orenstein, Paulo and Ramos, Thiago and Roma… (2024) Split Conformal Prediction and Non-Exchangeable Data | 0.843 | 4 | 4 | 75% |
| 2 | Ajroldi, Niccolò and Diquigiovanni, Jacopo and Fontana, Matteo and V… (2023) Conformal prediction bands for two-dimensional functional time series self | 0.693 | 6 | 1 | 100% |
| 3 | Diquigiovanni, Jacopo and Fontana, Matteo and Vantini, Simone (2024) Distribution-Free Prediction Bands for Multivariate Functional Time Series: an Application to the Italian Gas Market self | 0.644 | 4 | 1 | 100% |
| 4 | Barber, Rina Foygel and Candès, Emmanuel J. and Ramdas, Aaditya and… (2023) Conformal prediction beyond exchangeability | 0.644 | 3 | 2 | 67% |
| 5 | Gibbs, Isaac and Candes, Emmanuel (2021) Adaptive Conformal Inference Under Distribution Shift | 0.644 | 3 | 2 | 67% |
| 6 | Izbicki, Rafael and Shimizu, Gilson and Stern, Rafael B (2022) CD-split and HPD-split: Efficient Conformal Regions in High Dimensions | 0.644 | 2 | 2 | 100% |
| 7 | Xu, Chen and Xie, Yao (2021) Conformal prediction interval for dynamic time-series | 0.585 | 3 | 1 | 100% |
| 8 | Farinhas, António and Zerva, Chrysoula and Ulmer, Dennis and Martins… (2024) Non-Exchangeable Conformal Risk Control | 0.511 | 2 | 2 | 50% |
| 9 | Chernozhukov, Victor and Wüthrich, Kaspar and Yinchu, Zhu (2018) Exact and Robust Conformal Inference Methods for Predictive Machine Learning with Dependent Data | 0.511 | 2 | 1 | 100% |
| 10 | Zaffran, Margaux and Feron, Olivier and Goude, Yannig and Josse, Jul… (2022) Adaptive Conformal Predictions for Time Series | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 37 scored citations.