ExtremeConformal: Extreme Conformal Prediction Intervals

This new extreme conformal prediction framework provides informative prediction intervals at the high-confidence levels for which classical conformal methods fail. Conformal prediction is a popular method to construct prediction intervals with marginal coverage guarantees from black-box machine learning models. In applications with potentially high-impact events, a very high level of confidence is often required for predictions. If that level is too large relative to the amount of data used for calibration, classical conformal methods provide infinitely wide, thus, uninformative prediction intervals. Our extreme conformal procedure bridges extreme value statistics and conformal prediction to provide reliable and informative prediction intervals with high-confidence coverage, which can be constructed using any black-box extreme quantile regression method. A weighted version of the approach can account for nonstationary data. The methodology was introduced in Pasche, Lam, and Engelke (2026).

Links

ExtremeConformal documentation: https://opasche.github.io/ExtremeConformal/
ExtremeConformal source code: https://github.com/opasche/ExtremeConformal

Related article: https://doi.org/10.1007/s10687-026-00536-9 (PDF)
Preprint: https://arxiv.org/abs/2505.08578 (PDF)

Installation

To install the development version of ExtremeConformal from R, run

# install.packages("devtools")
devtools::install_github("opasche/ExtremeConformal")

References

Pasche, O. C., Lam, H., and Engelke, S. (2026). “Extreme Conformal Prediction: Reliable Intervals for High-Impact Events.” Extremes. https://doi.org/10.1007/s10687-026-00536-9