Порівняння методів

	Метод Кростона для переривчастого попиту	Модель ARIMA (Авторегресійна інтегрована ковзна середня)	Метод Тета
Галузь	Економетрика	Економетрика	Економетрика
Родина	Regression model	Regression model	Regression model
Рік появи	1972	2015	2000
Автор методу	J. D. Croston (1972)	Box & Jenkins (Box-Jenkins methodology)	Assimakopoulos & Nikolopoulos
Тип	Intermittent demand time-series forecasting	Univariate time-series model	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.	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	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	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	theta model, theta forecasting, Theta Yöntemi — M3 Tahmin Yarışması Birincisi
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).	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.