Usporedite metode

	STL dekompozicija: sezonska-trendna dekompozicija pomoću Loess-a	Model ARIMA (Autoregresivni integrirani pokretni prosjek)
Područje	Ekonometrija	Ekonometrija
Obitelj	Process / pipeline	Regression model
Godina nastanka	1990	2015
Tvorac	Cleveland, Cleveland, McRae & Terpenning	Box & Jenkins (Box-Jenkins methodology)
Vrsta	nonparametric iterative smoother	Univariate time-series model
Temeljni izvor	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
Drugi nazivi	Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Srodne	3	5
Sažetak	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).