Σύγκριση μεθόδων

	Απλή και Διπλή Εκθετική Εξομάλυνση (SES / Holt)	Μοντέλο ARIMA (Autoregressive Integrated Moving Average)	Μοντέλο Χώρου Καταστάσεων (Φίλτρο Kalman)
Πεδίο	Οικονομετρία	Οικονομετρία	Οικονομετρία
Οικογένεια	Regression model	Regression model	Regression model
Έτος προέλευσης	1957	2015	1990
Δημιουργός	Robert G. Brown (SES); Charles C. Holt (linear trend)	Box & Jenkins (Box-Jenkins methodology)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Τύπος	Exponential smoothing forecasting model	Univariate time-series model	State space time series model
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	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)
Συναφείς	3	5	4
Σύνοψη	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.