Сравнение на методи

	Кръстосана валидация за времеви редове (плъзгащ се/разширяващ се прозорец)	Бутстрап извод	Тест на Диболд-Мариано за равна прогнозна точност
Област	Иконометрия	Статистика	Иконометрия
Семейство	Process / pipeline	Regression model	Hypothesis test
Година на възникване	2012	1979	1995
Създател	Christoph Bergmeir & José Benítez	Bradley Efron	Francis Diebold & Roberto Mariano
Тип	Forecast evaluation procedure	Resampling-based inference	Non-parametric forecast comparison test
Основополагащ източник	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.	Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263.
Други названия	Rolling-Origin Cross-Validation, Walk-Forward Validation, Expanding Window Evaluation, Zaman Serisi Çapraz Doğrulama	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	DM Test, Test of Equal Forecast Accuracy, Diebold-Mariano Forecast Comparison Test, Tahmin Doğruluğu Eşitliği Testi
Свързани	3	5	3
Резюме	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.	The Diebold-Mariano (DM) test, introduced by Diebold and Mariano in 1995, is a widely used non-parametric procedure for formally comparing the predictive accuracy of two competing forecasting models. It evaluates whether the difference in forecast errors between two models is statistically significant, without requiring nested models or specific distributional assumptions about the forecasts, making it broadly applicable across economics, finance, and time-series analysis.