方法对比

	简单和双指数平滑 (SES / Holt)	广义自回归条件异方差模型 (GARCH)	季节性ARIMA（SARIMA）
领域	计量经济学	计量经济学	计量经济学
方法族	Regression model	Regression model	Regression model
起源年份	1957	1986	2015
提出者	Robert G. Brown (SES); Charles C. Holt (linear trend)	Tim Bollerslev	Box & Jenkins (seasonal extension of ARIMA)
类型	Exponential smoothing forecasting model	Conditional volatility model	Seasonal time-series model
开创性文献	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.	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
别名	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	seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA
相关	3	5	5
摘要	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.	SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period.