Vertaile menetelmiä

	Theta-menetelmä	OLS-regressio (Ordinary Least Squares)
Tieteenala	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2000	2019
Kehittäjä	Assimakopoulos & Nikolopoulos	Wooldridge (textbook treatment); classical least squares
Tyyppi	Univariate time-series forecasting model	Linear regression
Alkuperäislähde	Assimakopoulos, V. & Nikolopoulos, K. (2000). The Theta Model: A Decomposition Approach to Forecasting. International Journal of Forecasting, 16(4), 521-530.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Rinnakkaisnimet	theta model, theta forecasting, Theta Yöntemi — M3 Tahmin Yarışması Birincisi	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liittyvät	4	5
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.	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).