विधियों की तुलना करें

	सरल और दोहरा घातीय समतलन (SES / Holt)	स्टेट स्पेस मॉडल (कलमन फिल्टर)
क्षेत्र	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1957	1990
प्रवर्तक	Robert G. Brown (SES); Charles C. Holt (linear trend)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
प्रकार	Exponential smoothing forecasting model	State space time series model
मौलिक स्रोत	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.
उपनाम	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)
संबंधित	3	4
सारांश	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.