Compara mètodes

	ETS: Error, Tendència, Suavització Exponencial Estacional	Model d'ARIMA (Autoregressive Integrated Moving Average)	Model d'espai d'estats (Filtre de Kalman)
Camp	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model
Any d'origen	2008	2015	1990
Autor original	Hyndman, Koehler, Ord & Snyder (state space framework)	Box & Jenkins (Box-Jenkins methodology)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Tipus	Exponential smoothing state space model	Univariate time-series model	State space time series model
Font seminal	Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer.	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.
Àlies	exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Relacionats	5	5	4
Resum	ETS is a comprehensive exponential smoothing framework that automatically selects additive or multiplicative combinations of the error (E), trend (T) and seasonal (S) components of a time series. Formalised as an innovations state space model by Hyndman, Koehler, Ord and Snyder in 2008, it unifies and generalises the Holt-Winters family of forecasting methods.	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.