পদ্ধতির তুলনা করুন

	সময়-ক্রমিক ক্রস-ভ্যালিডেশন (রোলিং/এক্সপ্যান্ডিং উইন্ডো)	ARIMA (Autoregressive Integrated Moving Average) মডেল	Diebold-Mariano Test
ক্ষেত্র	অর্থমিতি	অর্থমিতি	অর্থমিতি
পরিবার	Process / pipeline	Regression model	Hypothesis test
উদ্ভবের বছর	2012	2015	1995
প্রবর্তক	Christoph Bergmeir & José Benítez	Box & Jenkins (Box-Jenkins methodology)	Francis Diebold & Roberto Mariano
ধরন	Forecast evaluation procedure	Univariate time-series model	Non-parametric forecast comparison test
মৌলিক উৎস	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	Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263.
অপর নাম	Rolling-Origin Cross-Validation, Walk-Forward Validation, Expanding Window Evaluation, Zaman Serisi Çapraz Doğrulama	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	DM Test, Test of Equal Forecast Accuracy, Diebold-Mariano Forecast Comparison Test, Tahmin Doğruluğu Eşitliği Testi
সম্পর্কিত	3	5	3
সারসংক্ষেপ	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).	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.