Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Modelul ARIMA (Autoregresiv Integrat cu Medii Mobile)× | Testul Durbin-Watson pentru Autocorelație× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 2015 | 1950 |
| Autorul original≠ | Box & Jenkins (Box-Jenkins methodology) | James Durbin & Geoffrey Watson |
| Tip≠ | Univariate time-series model | Test for first-order residual autocorrelation |
| Sursa seminală≠ | 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. DOI ↗ |
| Denumiri alternative | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | DW test, Durbin-Watson statistic, Durbin-Watson otokorelasyon testi |
| Înrudite≠ | 5 | 4 |
| Rezumat≠ | 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. |
| ScholarGateSet de date ↗ |
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