방법 비교

	단순 및 이중 지수 평활법 (SES / Holt)	일반화 자기회귀 조건부 이분산성 (GARCH)	최소제곱법(OLS) 회귀
분야	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model
기원 연도	1957	1986	2019
창시자	Robert G. Brown (SES); Charles C. Holt (linear trend)	Tim Bollerslev	Wooldridge (textbook treatment); classical least squares
유형	Exponential smoothing forecasting model	Conditional volatility model	Linear regression
원전	Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
별칭	SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
관련	3	5	5
요약	Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta.	GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns.	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).