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Bonjour, Quarto!
AdaptiveConformal: An R
Package for Adaptive Conformal Inference
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.
July 18, 2024
August 9, 2024
Conformal inference, Adaptive conformal inference, time series, R
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.
Ce document est un exemple de fichier Quarto Markdown (.qmd) qui utilise Python, Jupyter, nbformat et numpy.
{'language_info': {'name': 'python'}}
Tableau numpy: [1 2 3 4 5]
Moyenne: 3.0
Écart type: 1.4142135623730951
@article{susmann2024,
author = {Susmann, Herbert and Chambaz, Antoine and Josse, Julie},
publisher = {Société Française de Statistique},
title = {AdaptiveConformal: {An} {`R`} {Package} for {Adaptive}
{Conformal} {Inference}},
journal = {Computo},
date = {2024-07-18},
url = {https://computo.sfds.asso.fr/template-computo-quarto},
doi = {10.57750/edan-5f53},
issn = {2824-7795},
langid = {en},
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.}
}
---
title: "AdaptiveConformal: An `R` Package for Adaptive Conformal Inference"
subtitle: "AdaptiveConformal: An `R` Package for Adaptive Conformal Inference"
author:
- name: Herbert Susmann
corresponding: true
email: herbps10@gmail.com
url: https://herbsusmann.com
orcid: 0000-0002-3540-8255
affiliations:
- name: CEREMADE (UMR 7534), Université Paris-Dauphine PSL, Place du Maréchal de Lattre de Tassigny, Paris, 75016, France
url: https://www.ceremade.dauphine.fr/
- name: Antoine Chambaz
email: antoine.chambaz@u-paris.fr
url: https://helios2.mi.parisdescartes.fr/~chambaz/
orcid: 0000-0002-5592-6471
affiliations:
- name: Université Paris Cité, CNRS, MAP5, F-75006 Paris, France
department:
url: https://map5.mi.parisdescartes.fr/
- name: Julie Josse
email: julie.josse@inria.fr
url: http://juliejosse.com/
orcid: 0000-0001-9547-891X
affiliations:
- name: Inria PreMeDICaL team, Idesp, Université de Montpellier
url: https://team.inria.fr/premedical/
date: 07-18-2024
date-modified: last-modified
description: |
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.
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.
keywords: [Conformal inference, Adaptive conformal inference, time series, R]
citation:
type: article-journal
container-title: "Computo"
doi: "10.57750/edan-5f53"
url: https://computo.sfds.asso.fr/template-computo-quarto
publisher: "Société Française de Statistique"
issn: "2824-7795"
bibliography: references.bib
github-user: computorg
logo: "true"
repo: "published-202407-susmann-adaptive-conformal"
draft: false # set to false once the build is running
published: true # will be set to true once accepted
google-scholar: true
format:
computo-html:
code-fold: true
computo-pdf:
keep-tex: true
include-in-header:
- text: |
\usepackage{stmaryrd}
\usepackage{xfrac}
---
# Introduction
Ce document est un exemple de fichier Quarto Markdown (.qmd) qui utilise Python, Jupyter, nbformat et numpy.
# Utilisation de Python
```{python}
print("Bonjour, Quarto!")
```
```{python}
import nbformat
from nbformat import read
# Exemple de lecture d'un notebook Jupyter
notebook_path = 'notebook.ipynb'
with open(notebook_path, 'r', encoding='utf-8') as f:
nb = read(f, as_version=4)
# Afficher les métadonnées du notebook
print(nb.metadata)
```
```{python}
import numpy as np
# Création d'un tableau numpy
a = np.array([1, 2, 3, 4, 5])
print("Tableau numpy:", a)
# Calcul de la moyenne
moyenne = np.mean(a)
print("Moyenne:", moyenne)
# Calcul de l'écart type
ecart_type = np.std(a)
print("Écart type:", ecart_type)
```