Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Njia ya Croston ya Mahitaji Yanayokatizwa× | Uchanganuzi wa Poisson na Negative Binomial× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1972 | 1998 |
| Mwanzilishi≠ | J. D. Croston (1972) | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Aina≠ | Intermittent demand time-series forecasting | Generalized linear model for count data |
| Chanzo asilia≠ | Croston, J. D. (1972). Forecasting and Stock Control for Intermittent Demands. Operational Research Quarterly, 23(3), 289-303. DOI ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Majina mbadala≠ | Croston method, intermittent demand forecasting, Croston Yöntemi — Aralıklı Talep Tahmini | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Zinazohusiana | 4 | 4 |
| Muhtasari≠ | 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. | Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred. |
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