Comparar métodos

	ETS: Erro, Tendência, Suavização Exponencial Sazonal	Regressão por Mínimos Quadrados Ordinários (MQO)	Modelo de Espaço de Estados (Filtro de Kalman)
Área	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model
Ano de origem	2008	2019	1990
Autor original	Hyndman, Koehler, Ord & Snyder (state space framework)	Wooldridge (textbook treatment); classical least squares	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Tipo	Exponential smoothing state space model	Linear regression	State space time series model
Fonte seminal	Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Outros nomes	exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Relacionados	5	5	4
Resumo	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.