https://github.com/computorg/published-202407-susmann-adaptive-conformal

https://github.com/computorg/published-202407-susmann-adaptive-conformal

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• Host: GitHub
• URL: https://github.com/computorg/published-202407-susmann-adaptive-conformal
• Owner: computorg
• License: cc-by-4.0
• Created: 2024-07-12T15:42:33.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2025-07-29T07:44:34.000Z (11 months ago)
• Last Synced: 2025-07-29T09:42:42.660Z (11 months ago)
• Language: TeX
• Homepage: http://computo-journal.org/published-202407-susmann-adaptive-conformal/
• Size: 10.5 MB
• Stars: 0
• Watchers: 4
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# AdaptiveConformal: An `R` Package for Adaptive Conformal Inference

Herbert Susmann, Antoine Chambaz, Julie Josse

2024-07-18

### Citation

Herbert Susmann, Antoine Chambaz and Julie Josse (July 2024). AdaptiveConformal: An R Package for Adaptive Conformal Inference. Computo.

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### Authors’ affiliations

- [Herbert Susmann](https://herbsusmann.com) (CEREMADE (UMR 7534), Université Paris-Dauphine PSL, Place du Maréchal de Lattre de Tassigny, Paris, 75016, France)

- [Antoine Chambaz](https://helios2.mi.parisdescartes.fr/~chambaz/) (Université Paris Cité, CNRS, MAP5, F-75006 Paris, France)

- [Julie Josse](http://juliejosse.com/) (Inria PreMeDICaL team, Idesp, Université de Montpellier)

### Abstract

Conformal Inference (CI) is a popular approach for generating finite

sample prediction intervals based on the output of any point prediction

method when data are exchangeable. Adaptive Conformal Inference (ACI)

algorithms extend CI to the case of sequentially observed data, such as

time series, and exhibit strong theoretical guarantees without having to

assume exchangeability of the observed data. The common thread that

unites algorithms in the ACI family is that they adaptively adjust the

width of the generated prediction intervals in response to the observed

data. We provide a detailed description of five ACI algorithms and their

theoretical guarantees, and test their performance in simulation

studies. We then present a case study of producing prediction intervals

for influenza incidence in the United States based on black-box point

forecasts. Implementations of all the algorithms are released as an

open-source `R` package, `AdaptiveConformal`, which also includes tools

for visualizing and summarizing conformal prediction intervals.