Salīdzināt metodes

	STL sadalīšana: Sezonālās-trendu sadalīšana, izmantojot Loess	ARIMA (autoregresīvais integrētais slīdošā vidējā) modelis
Nozare	Ekonometrija	Ekonometrija
Saime	Process / pipeline	Regression model
Izcelsmes gads	1990	2015
Autors	Cleveland, Cleveland, McRae & Terpenning	Box & Jenkins (Box-Jenkins methodology)
Tips	nonparametric iterative smoother	Univariate time-series model
Pirmavots	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
Citi nosaukumi	Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Saistītās	3	5
Kopsavilkums	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).