手法を比較

	単純指数平滑法（SES）およびホルト法（Double Exponential Smoothing）	ARIMA（自己回帰和分移動平均）モデル	状態空間モデル（カルマンフィルタ）	構造的時系列モデル（基本構造モデル）
分野	計量経済学	計量経済学	計量経済学	計量経済学
系統	Regression model	Regression model	Regression model	Regression model
提唱年	1957	2015	1990	1990
提唱者	Robert G. Brown (SES); Charles C. Holt (linear trend)	Box & Jenkins (Box-Jenkins methodology)	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Andrew C. Harvey
種類	Exponential smoothing forecasting model	Univariate time-series model	State space time series model	State-space (unobserved components) time series model
原典	Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.	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	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737
別名	SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)
関連	3	5	4	4
概要	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.	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).	A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.	The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit.