Bandingkan metode

	Metode Theta	Model ARIMA (Autoregressive Integrated Moving Average)	ETS: Perataan Eksponensial Kesalahan, Tren, Musiman	Penghalusan Eksponensial Tiga Kali Lipat Holt-Winters	Regresi Kuadrat Terkecil Biasa (Ordinary Least Squares - OLS)
Bidang	Ekonometrika	Ekonometrika	Ekonometrika	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model	Regression model	Regression model	Regression model
Tahun asal	2000	2015	2008	1960	2019
Pencetus	Assimakopoulos & Nikolopoulos	Box & Jenkins (Box-Jenkins methodology)	Hyndman, Koehler, Ord & Snyder (state space framework)	Charles C. Holt and Peter R. Winters	Wooldridge (textbook treatment); classical least squares
Tipe	Univariate time-series forecasting model	Univariate time-series model	Exponential smoothing state space model	Exponential smoothing forecasting model	Linear regression
Sumber perintis	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	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
Alias	theta model, theta forecasting, Theta Yöntemi — M3 Tahmin Yarışması Birincisi	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	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
Terkait	4	5	5	4	5
Ringkasan	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).	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).