Confronta i metodi

	Il Metodo Theta	Modello ARIMA (Autoregressive Integrated Moving Average)	Regression with Ordinary Least Squares (OLS)
Campo	Econometria	Econometria	Econometria
Famiglia	Regression model	Regression model	Regression model
Anno di origine	2000	2015	2019
Ideatore	Assimakopoulos & Nikolopoulos	Box & Jenkins (Box-Jenkins methodology)	Wooldridge (textbook treatment); classical least squares
Tipo	Univariate time-series forecasting model	Univariate time-series model	Linear regression
Fonte seminale	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	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	theta model, theta forecasting, Theta Yöntemi — M3 Tahmin Yarışması Birincisi	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Correlati	4	5	5
Sintesi	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).	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).