Bandingkan metode

	Penghalusan Eksponensial Sederhana dan Ganda (SES / Holt)	Model Ruang Keadaan (Kalman Filter)	Model Deret Waktu Struktural (Model Struktural Dasar)
Bidang	Ekonometrika	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model	Regression model
Tahun asal	1957	1990	1990
Pencetus	Robert G. Brown (SES); Charles C. Holt (linear trend)	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Andrew C. Harvey
Tipe	Exponential smoothing forecasting model	State space time series model	State-space (unobserved components) time series model
Sumber perintis	Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.	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
Alias	SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)	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)
Terkait	3	4	4
Ringkasan	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.	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.