Σύγκριση μεθόδων

	Η Μέθοδος Theta	Τριπλή Εκθετική Εξομάλυνση Holt-Winters	Παλινδρόμηση Ελαχίστων Τετραγώνων (OLS)
Πεδίο	Οικονομετρία	Οικονομετρία	Οικονομετρία
Οικογένεια	Regression model	Regression model	Regression model
Έτος προέλευσης	2000	1960	2019
Δημιουργός	Assimakopoulos & Nikolopoulos	Charles C. Holt and Peter R. Winters	Wooldridge (textbook treatment); classical least squares
Τύπος	Univariate time-series forecasting model	Exponential smoothing forecasting model	Linear regression
Θεμελιώδης πηγή	Assimakopoulos, V. & Nikolopoulos, K. (2000). The Theta Model: A Decomposition Approach to Forecasting. International Journal of Forecasting, 16(4), 521-530.	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
Εναλλακτικές ονομασίες	theta model, theta forecasting, Theta Yöntemi — M3 Tahmin Yarışması Birincisi	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
Συναφείς	4	4	5
Σύνοψη	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.	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).