השוואת שיטות

	פירוק STL: פירוק עונתי-מגמה באמצעות Loess	מודל ARIMA (Autoregressive Integrated Moving Average)
תחום	אקונומטריקה	אקונומטריקה
משפחה	Process / pipeline	Regression model
שנת המקור	1990	2015
הוגה השיטה	Cleveland, Cleveland, McRae & Terpenning	Box & Jenkins (Box-Jenkins methodology)
סוג	nonparametric iterative smoother	Univariate time-series model
מקור מכונן	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
כינויים	Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
קשורות	3	5
תקציר	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).