Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data
Open accessAbstract
We extend conformal inference to general settings that allow for time series data. Our proposal is developed as a randomization method and accounts for potential serial dependence by including block structures in the permutation scheme. As a result, the proposed method retains the exact, model-free validity when the data are i.i.d. or more generally exchangeable, similar to usual conformal inference methods. When exchangeability fails, as is the case for common time series data, the proposed approach is approximately valid under weak assumptions on the conformity score.
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If residuals from the boosting analysis are not temporally dependent, the exchangeability assumptions required for conformal prediction regions can still apply despite temporal dependence in the multiple time series data.
“A complication is that the temporal dependence in the multiple time series data violates the requisite exchangeability (Chernozhukov et al., 2018). However, if the residuals from the boosting analysis are not temporally dependent, the exchangeability assumptions can still apply (Berk et al., 2025).”
From Forecasting Extreme High Summer Temperatures in Paris and Cairo Using Gradient Boosting and Conformal Prediction Regions (2025) · cites this paper as Chernozhukov et al., 2018
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- Authors
- Chernozhukov, V., Wüthrich, K., & Zhu, Y.
- Publisher
- The IFS
- Published
- 2018-03-02
- OpenAlex
- W2788786250
- Cited by
- 57
- References
- 16
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 8 · 2023: 8 · 2022: 8 · 2021: 8 · 2020: 8 · 2019: 2 · 2018: 1
- Type
- report
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Econstor (Econstor) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- conformal prediction, time series, serial dependence, block permutation, exchangeability, randomization, predictive inference
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