Vertaile menetelmiä

	ETS: Virhe-, trendi- ja kausitasoitus	Rakenteellinen aikasarjamalli (Perusrakennemalli)
Tieteenala	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2008	1990
Kehittäjä	Hyndman, Koehler, Ord & Snyder (state space framework)	Andrew C. Harvey
Tyyppi	Exponential smoothing state space model	State-space (unobserved components) time series model
Alkuperäislähde	Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737
Rinnakkaisnimet	exponential smoothing state space model, innovations state space model, Holt-Winters family, ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)
Liittyvät	5	4
Tiivistelmä	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.	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.