방법 비교

	간헐적 수요를 위한 크로스턴 방법	ARIMA (Autoregressive Integrated Moving Average) 모형	포아송 및 음이항 회귀분석	세타 방법(The Theta Method)
분야	계량경제학	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model	Regression model
기원 연도	1972	2015	1998	2000
창시자	J. D. Croston (1972)	Box & Jenkins (Box-Jenkins methodology)	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)	Assimakopoulos & Nikolopoulos
유형	Intermittent demand time-series forecasting	Univariate time-series model	Generalized linear model for count data	Univariate time-series forecasting model
원전	Croston, J. D. (1972). Forecasting and Stock Control for Intermittent Demands. Operational Research Quarterly, 23(3), 289-303.	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.	Assimakopoulos, V. & Nikolopoulos, K. (2000). The Theta Model: A Decomposition Approach to Forecasting. International Journal of Forecasting, 16(4), 521-530.
별칭	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	theta model, theta forecasting, Theta Yöntemi — M3 Tahmin Yarışması Birincisi
관련	4	5	4	4
요약	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.	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.