Methoden vergleichen

	LOESS / LOWESS Lokale Regression	X-13ARIMA-SEATS Saisonbereinigung
Fachgebiet	Maschinelles Lernen	Ökonometrie
Familie	Machine learning	Process / pipeline
Entstehungsjahr	1979	1998
Urheber	William S. Cleveland	U.S. Census Bureau; Findley et al.
Typ	Local nonparametric regression smoother	Non-parametric / model-based hybrid
Wegweisende Quelle	Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836.	Findley, D. F., Monsell, B. C., Bell, W. R., Otto, M. C., & Chen, B.-C. (1998). New capabilities and methods of the X-12-ARIMA seasonal adjustment program. Journal of Business & Economic Statistics, 16(2), 127–152.
Aliasnamen	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon	X-13ARIMA-SEATS, X-12-ARIMA, Census X-13, Mevsimsel Düzeltme X-13
Verwandt	3	3
Zusammenfassung	LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots.	X-13ARIMA-SEATS is the standard seasonal adjustment program produced by the U.S. Census Bureau, combining RegARIMA pre-adjustment with either the classical X-11 filter or the model-based SEATS signal-extraction algorithm. It is the official tool used by national statistical agencies worldwide — including Eurostat and the U.S. Bureau of Labor Statistics — to remove recurring calendar and seasonal patterns from monthly or quarterly economic time series such as GDP, employment, and retail sales.