পদ্ধতির তুলনা করুন

	ARIMA (Autoregressive Integrated Moving Average) মডেল	Durbin-Watson Autocorrelation Test
ক্ষেত্র	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	2015	1950
প্রবর্তক	Box & Jenkins (Box-Jenkins methodology)	James Durbin & Geoffrey Watson
ধরন	Univariate time-series model	Test for first-order residual autocorrelation
মৌলিক উৎস	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	Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409–428.
অপর নাম	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	DW test, Durbin-Watson statistic, Durbin-Watson otokorelasyon testi
সম্পর্কিত	5	4
সারসংক্ষেপ	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 Durbin-Watson test, developed by James Durbin and Geoffrey Watson in 1950–1951, detects first-order serial correlation in the residuals of a linear regression. Its statistic ranges from 0 to 4, with a value near 2 indicating no autocorrelation, values toward 0 indicating positive autocorrelation, and values toward 4 indicating negative autocorrelation. It remains one of the most reported regression diagnostics despite well-known limitations.