विधियों की तुलना करें

	समय-श्रृंखला क्रॉस-वैलिडेशन (रोलिंग/एक्सपैंडिंग विंडो)	बूटस्ट्रैप अनुमान
क्षेत्र	अर्थमिति	सांख्यिकी
परिवार	Process / pipeline	Regression model
उद्भव वर्ष	2012	1979
प्रवर्तक	Christoph Bergmeir & José Benítez	Bradley Efron
प्रकार	Forecast evaluation procedure	Resampling-based inference
मौलिक स्रोत	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.
उपनाम	Rolling-Origin Cross-Validation, Walk-Forward Validation, Expanding Window Evaluation, Zaman Serisi Çapraz Doğrulama	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı
संबंधित	3	5
सारांश	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.