Krahasoni metodat

	Lëmuesja e thjeshtë dhe e dyfishtë (SES / Holt)	Model i Hapësirës së Gjendjeve (Filtri Kalman)	Modeli Strukturor i Serive Kohore (Modeli Strukturor Bazë)
Fusha	Ekonometri	Ekonometri	Ekonometri
Familja	Regression model	Regression model	Regression model
Viti i origjinës	1957	1990	1990
Krijuesi	Robert G. Brown (SES); Charles C. Holt (linear trend)	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Andrew C. Harvey
Lloji	Exponential smoothing forecasting model	State space time series model	State-space (unobserved components) time series model
Burimi themelues	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
Emërtime të tjera	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)
Të lidhura	3	4	4
Përmbledhja	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.