Comparer des méthodes

	Validation croisée sur séries temporelles (fenêtre glissante/extensible)	Inférence par bootstrap
Domaine	Économétrie	Statistique
Famille	Process / pipeline	Regression model
Année d'origine	2012	1979
Auteur d'origine	Christoph Bergmeir & José Benítez	Bradley Efron
Type	Forecast evaluation procedure	Resampling-based inference
Source fondatrice	Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192–213.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.
Alias	Rolling-Origin Cross-Validation, Walk-Forward Validation, Expanding Window Evaluation, Zaman Serisi Çapraz Doğrulama	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı
Apparentées	3	5
Résumé	Time-series cross-validation is a resampling procedure designed for sequentially ordered data. Instead of randomly partitioning observations — which would destroy temporal structure and introduce data leakage — it advances a forecast origin one step at a time, fitting a model on all past data up to that origin and evaluating it on the immediately following out-of-sample period. Economists, financial analysts, and meteorologists use it whenever an honest, operationally realistic estimate of predictive accuracy is required for a time-ordered process.	Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples.