Vertaile menetelmiä

	STL-hajotelma: Kausivaihtelun ja trendin hajotelma Loess-menetelmällä	ARIMA (Autoregressive Integrated Moving Average) -malli
Tieteenala	Ekonometria	Ekonometria
Menetelmäperhe	Process / pipeline	Regression model
Syntyvuosi	1990	2015
Kehittäjä	Cleveland, Cleveland, McRae & Terpenning	Box & Jenkins (Box-Jenkins methodology)
Tyyppi	nonparametric iterative smoother	Univariate time-series model
Alkuperäislähde	Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3–73.	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
Rinnakkaisnimet	Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Liittyvät	3	5
Tiivistelmä	STL Decomposition, introduced by Cleveland, Cleveland, McRae, and Terpenning (1990), is a nonparametric procedure that separates a time series into three additive components — trend, seasonal, and remainder — using iterative locally weighted regression (loess). Widely used in economics, meteorology, and data science, it handles time series of any periodicity and is robust to the presence of outliers, making it a highly flexible alternative to classical decomposition 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).