Порівняння методів

	Метод Кростона для переривчастого попиту	Регресія звичайно найменших квадратів (ЗНК)	Метод Тета
Галузь	Економетрика	Економетрика	Економетрика
Родина	Regression model	Regression model	Regression model
Рік появи	1972	2019	2000
Автор методу	J. D. Croston (1972)	Wooldridge (textbook treatment); classical least squares	Assimakopoulos & Nikolopoulos
Тип	Intermittent demand time-series forecasting	Linear regression	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.	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.
Інші назви	Croston method, intermittent demand forecasting, Croston Yöntemi — Aralıklı Talep Tahmini	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	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.	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.