قارن الطرق

	تحليل STL: تحليل الاتجاه والموسمية باستخدام الانحدار الموزون محليًا (Loess)	الانحدار المحلي LOESS / LOWESS	التعديل الموسمي باستخدام X-13ARIMA-SEATS
المجال	الاقتصاد القياسي	تعلم الآلة	الاقتصاد القياسي
العائلة	Process / pipeline	Machine learning	Process / pipeline
سنة النشأة	1990	1979	1998
صاحب الطريقة	Cleveland, Cleveland, McRae & Terpenning	William S. Cleveland	U.S. Census Bureau; Findley et al.
النوع	nonparametric iterative smoother	Local nonparametric regression smoother	Non-parametric / model-based hybrid
المصدر التأسيسي	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.	Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836.	Findley, D. F., Monsell, B. C., Bell, W. R., Otto, M. C., & Chen, B.-C. (1998). New capabilities and methods of the X-12-ARIMA seasonal adjustment program. Journal of Business & Economic Statistics, 16(2), 127–152.
الأسماء البديلة	Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL)	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon	X-13ARIMA-SEATS, X-12-ARIMA, Census X-13, Mevsimsel Düzeltme X-13
ذات صلة	3	3	3
الملخص	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.	LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots.	X-13ARIMA-SEATS is the standard seasonal adjustment program produced by the U.S. Census Bureau, combining RegARIMA pre-adjustment with either the classical X-11 filter or the model-based SEATS signal-extraction algorithm. It is the official tool used by national statistical agencies worldwide — including Eurostat and the U.S. Bureau of Labor Statistics — to remove recurring calendar and seasonal patterns from monthly or quarterly economic time series such as GDP, employment, and retail sales.