Módszerek összehasonlítása

	STL-dekompozíció: Szezonális-trend dekompozíció loess-szel	ARIMA (Autoregressive Integrated Moving Average) modell
Tudományterület	Ökonometria	Ökonometria
Módszercsalád	Process / pipeline	Regression model
Keletkezés éve	1990	2015
Megalkotó	Cleveland, Cleveland, McRae & Terpenning	Box & Jenkins (Box-Jenkins methodology)
Típus	nonparametric iterative smoother	Univariate time-series model
Alapmű	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
Alternatív nevek	Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Kapcsolódó	3	5
Összefoglaló	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).