পদ্ধতির তুলনা করুন

	হড্রিক-প্রেস্কট ফিল্টার: সামষ্টিক অর্থনৈতিক সময় সিরিজের প্রবণতা-চক্র বিভাজন	STL Decomposition: Seasonal-Trend Decomposition using Loess
ক্ষেত্র	অর্থমিতি	অর্থমিতি
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1997	1990
প্রবর্তক	Robert Hodrick & Edward Prescott	Cleveland, Cleveland, McRae & Terpenning
ধরন	Penalized least-squares smoother	nonparametric iterative smoother
মৌলিক উৎস	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.
অপর নাম	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)
সম্পর্কিত	3	3
সারসংক্ষেপ	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.