Comparer des méthodes

	Conformal Prediction pour la prévision de séries temporelles	Régression quantile
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2021	1978
Auteur d'origine	Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)	Koenker & Bassett
Type	Distribution-free prediction interval wrapper	Conditional quantile regression
Source fondatrice	Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi)	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	4	5
Résumé	Conformal prediction is a distribution-free wrapper that turns any point forecaster — ARIMA, a neural network, or a machine-learning model — into valid prediction intervals using only its residuals. The time-series form was popularised by Xu & Xie (2021) and the modern tutorial treatment by Angelopoulos & Bates (2023).	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.