Salīdzināt metodes

	Laika sēriju krusteniskā validācija (slīdošais/paplašinošais logs)	ARIMA (autoregresīvais integrētais slīdošā vidējā) modelis	Bootstrap Inference
Nozare	Ekonometrija	Ekonometrija	Statistika
Saime	Process / pipeline	Regression model	Regression model
Izcelsmes gads	2012	2015	1979
Autors	Christoph Bergmeir & José Benítez	Box & Jenkins (Box-Jenkins methodology)	Bradley Efron
Tips	Forecast evaluation procedure	Univariate time-series model	Resampling-based inference
Pirmavots	Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192–213.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.
Citi nosaukumi	Rolling-Origin Cross-Validation, Walk-Forward Validation, Expanding Window Evaluation, Zaman Serisi Çapraz Doğrulama	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı
Saistītās	3	5	5
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.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	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.