Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Метод Кростона для переривчастого попиту× | Модель ARIMA (Авторегресійна інтегрована ковзна середня)× | Пуассонівська та від’ємна біноміальна регресія× | |
|---|---|---|---|
| Галузь | Економетрика | Економетрика | Економетрика |
| Родина | Regression model | Regression model | Regression model |
| Рік появи≠ | 1972 | 2015 | 1998 |
| Автор методу≠ | J. D. Croston (1972) | Box & Jenkins (Box-Jenkins methodology) | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Тип≠ | Intermittent demand time-series forecasting | Univariate time-series model | Generalized linear model for count data |
| Основоположне джерело≠ | 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 | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Інші назви≠ | Croston method, intermittent demand forecasting, Croston Yöntemi — Aralıklı Talep Tahmini | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Пов'язані≠ | 4 | 5 | 4 |
| Підсумок≠ | 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). | 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. |
| ScholarGateНабір даних ↗ |
|
|
|