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| Metoda Crostona dla popytu nieciągłego× | Model ARIMA (Autoregressive Integrated Moving Average)× | Regresja metodą najmniejszych kwadratów (OLS)× | |
|---|---|---|---|
| Dziedzina | Ekonometria | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model | Regression model |
| Rok powstania≠ | 1972 | 2015 | 2019 |
| Twórca≠ | J. D. Croston (1972) | Box & Jenkins (Box-Jenkins methodology) | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Intermittent demand time-series forecasting | Univariate time-series model | Linear regression |
| Źródło pierwotne≠ | Croston, J. D. (1972). Forecasting and Stock Control for Intermittent Demands. Operational Research Quarterly, 23(3), 289-303. DOI ↗ | 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 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Inne nazwy≠ | Croston method, intermittent demand forecasting, Croston Yöntemi — Aralıklı Talep Tahmini | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Pokrewne≠ | 4 | 5 | 5 |
| Podsumowanie≠ | Croston's method, introduced by J. D. Croston in 1972, is a time-series forecasting technique built for intermittent demand series in which periods of zero demand are frequent. Instead of forecasting the raw series, it models the size of demand when it occurs and the interval between demand occurrences as two separate processes. | 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). | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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