Sammenlign metoder

	Theta-metoden	ETS: Eksponentiel udjævning med fejl, trend og sæsonudsving	Holt-Winters tredobbelt eksponentiel udjævning	Almindelig mindste kvadraters metode (OLS) regression
Fagområde	Økonometri	Økonometri	Økonometri	Økonometri
Familie	Regression model	Regression model	Regression model	Regression model
Oprindelsesår	2000	2008	1960	2019
Ophavsperson	Assimakopoulos & Nikolopoulos	Hyndman, Koehler, Ord & Snyder (state space framework)	Charles C. Holt and Peter R. Winters	Wooldridge (textbook treatment); classical least squares
Type	Univariate time-series forecasting model	Exponential smoothing state space model	Exponential smoothing forecasting model	Linear regression
Oprindelig kilde	Assimakopoulos, V. & Nikolopoulos, K. (2000). The Theta Model: A Decomposition Approach to Forecasting. International Journal of Forecasting, 16(4), 521-530.	Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer.	Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Aliasser	theta model, theta forecasting, Theta Yöntemi — M3 Tahmin Yarışması Birincisi	exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme	triple exponential smoothing, Winters' method, Holt-Winters seasonal method, Holt-Winters Üçlü Üstel Düzleştirme	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relaterede	4	5	4	5
Resumé	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.	ETS is a comprehensive exponential smoothing framework that automatically selects additive or multiplicative combinations of the error (E), trend (T) and seasonal (S) components of a time series. Formalised as an innovations state space model by Hyndman, Koehler, Ord and Snyder in 2008, it unifies and generalises the Holt-Winters family of forecasting methods.	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.	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).