Vertaile menetelmiä

	Theta-menetelmä	ARIMA (Autoregressive Integrated Moving Average) -malli	Holt-Wintersin kolminkertainen eksponentiaalinen tasoitus
Tieteenala	Ekonometria	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	2000	2015	1960
Kehittäjä	Assimakopoulos & Nikolopoulos	Box & Jenkins (Box-Jenkins methodology)	Charles C. Holt and Peter R. Winters
Tyyppi	Univariate time-series forecasting model	Univariate time-series model	Exponential smoothing forecasting model
Alkuperäislähde	Assimakopoulos, V. & Nikolopoulos, K. (2000). The Theta Model: A Decomposition Approach to Forecasting. International Journal of Forecasting, 16(4), 521-530.	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	Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342.
Rinnakkaisnimet	theta model, theta forecasting, Theta Yöntemi — M3 Tahmin Yarışması Birincisi	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme
Liittyvät	4	5	4
Tiivistelmä	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.	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).	Holt-Winters triple exponential smoothing is a forecasting model that extends Holt's double smoothing by adding a seasonal component, introduced by Peter Winters in 1960 building on Charles Holt's work. It tracks three evolving quantities — level, trend, and season — and combines them to forecast a continuous time series.