Comparar métodos

	Método de Croston para Demanda Intermitente	Modelo ARIMA (Autoregressive Integrated Moving Average)	O Método Theta
Área	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model
Ano de origem	1972	2015	2000
Autor original	J. D. Croston (1972)	Box & Jenkins (Box-Jenkins methodology)	Assimakopoulos & Nikolopoulos
Tipo	Intermittent demand time-series forecasting	Univariate time-series model	Univariate time-series forecasting model
Fonte seminal	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.
Outros nomes	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
Relacionados	4	5	4
Resumo	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.