قارن الطرق

	فلتر هودريك-بريسكوت: تحليل الاتجاه والدورة للسلاسل الزمنية الاقتصادية الكلية	تحليل STL: تحليل الاتجاه والموسمية باستخدام الانحدار الموزون محليًا (Loess)
المجال	الاقتصاد القياسي	الاقتصاد القياسي
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	1997	1990
صاحب الطريقة	Robert Hodrick & Edward Prescott	Cleveland, Cleveland, McRae & Terpenning
النوع	Penalized least-squares smoother	nonparametric iterative smoother
المصدر التأسيسي	Hodrick, R. J., & Prescott, E. C. (1997). Postwar U.S. business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29(1), 1–16.	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.
الأسماء البديلة	Hodrick-Prescott Filter, HP Decomposition, Trend-Cycle Filter, HP Filtresi	Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL)
ذات صلة	3	3
الملخص	The Hodrick-Prescott (HP) filter is a penalized least-squares technique used in macroeconomics and empirical finance to decompose a time series into a smooth long-run trend component and a short-run cyclical component. Introduced by Hodrick and Prescott (1997) using postwar U.S. business cycle data, it has become one of the most widely applied filters in business cycle analysis, monetary policy research, and applied econometrics.	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.