विधियों की तुलना करें

	थीटा विधि	ऑटोरेग्रेसिव इंटीग्रेटेड मूविंग एवरेज (ARIMA) मॉडल	Holt-Winters ट्रिपल एक्सपोनेंशियल स्मूथिंग	साधारण न्यूनतम वर्ग (OLS) समाश्रयण
क्षेत्र	अर्थमिति	अर्थमिति	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model	Regression model	Regression model
उद्भव वर्ष	2000	2015	1960	2019
प्रवर्तक	Assimakopoulos & Nikolopoulos	Box & Jenkins (Box-Jenkins methodology)	Charles C. Holt and Peter R. Winters	Wooldridge (textbook treatment); classical least squares
प्रकार	Univariate time-series forecasting model	Univariate time-series model	Exponential smoothing forecasting model	Linear regression
मौलिक स्रोत	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	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
उपनाम	theta model, theta forecasting, Theta Yöntemi — M3 Tahmin Yarışması Birincisi	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	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
संबंधित	4	5	4	5
सारांश	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).	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).