Salīdzināt metodes

	Laika sēriju krusteniskā validācija (slīdošais/paplašinošais logs)	Dībolda-Mariāno tests par prognožu precizitātes līdzvērtību
Nozare	Ekonometrija	Ekonometrija
Saime	Process / pipeline	Hypothesis test
Izcelsmes gads	2012	1995
Autors	Christoph Bergmeir & José Benítez	Francis Diebold & Roberto Mariano
Tips	Forecast evaluation procedure	Non-parametric forecast comparison test
Pirmavots	Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192–213.	Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263.
Citi nosaukumi	Rolling-Origin Cross-Validation, Walk-Forward Validation, Expanding Window Evaluation, Zaman Serisi Çapraz Doğrulama	DM Test, Test of Equal Forecast Accuracy, Diebold-Mariano Forecast Comparison Test, Tahmin Doğruluğu Eşitliği Testi
Saistītās	3	3
Kopsavilkums	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.	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.