Порівняння методів

	Метод Кростона для переривчастого попиту	Пуассонівська та від’ємна біноміальна регресія	Метод Тета
Галузь	Економетрика	Економетрика	Економетрика
Родина	Regression model	Regression model	Regression model
Рік появи	1972	1998	2000
Автор методу	J. D. Croston (1972)	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)	Assimakopoulos & Nikolopoulos
Тип	Intermittent demand time-series forecasting	Generalized linear model for count data	Univariate time-series forecasting model
Основоположне джерело	Croston, J. D. (1972). Forecasting and Stock Control for Intermittent Demands. Operational Research Quarterly, 23(3), 289-303.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.	Assimakopoulos, V. & Nikolopoulos, K. (2000). The Theta Model: A Decomposition Approach to Forecasting. International Journal of Forecasting, 16(4), 521-530.
Інші назви	Croston method, intermittent demand forecasting, Croston Yöntemi — Aralıklı Talep Tahmini	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon	theta model, theta forecasting, Theta Yöntemi — M3 Tahmin Yarışması Birincisi
Пов'язані	4	4	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.	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.	The Theta Method is a univariate time-series forecasting model introduced by Assimakopoulos and Nikolopoulos in 2000. It decomposes a series into two theta lines that capture its long-run trend and its short-run dynamics, forecasts each line separately, and combines them by a weighted average. Its simplicity and accuracy made it the winner of the M3 forecasting competition.