方法对比

	Croston方法用于间歇性需求	ARIMA（自回归积分滑动平均）模型	普通最小二乘法 (OLS) 回归	Theta 方法
领域	计量经济学	计量经济学	计量经济学	计量经济学
方法族	Regression model	Regression model	Regression model	Regression model
起源年份	1972	2015	2019	2000
提出者	J. D. Croston (1972)	Box & Jenkins (Box-Jenkins methodology)	Wooldridge (textbook treatment); classical least squares	Assimakopoulos & Nikolopoulos
类型	Intermittent demand time-series forecasting	Univariate time-series model	Linear regression	Univariate time-series forecasting model
开创性文献	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	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Assimakopoulos, V. & Nikolopoulos, K. (2000). The Theta Model: A Decomposition Approach to Forecasting. International Journal of Forecasting, 16(4), 521-530.
别名	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	theta model, theta forecasting, Theta Yöntemi — M3 Tahmin Yarışması Birincisi
相关	4	5	5	4
摘要	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).	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.