পদ্ধতির তুলনা করুন

	Baxter-King Band-Pass Filter	STL Decomposition: Seasonal-Trend Decomposition using Loess
ক্ষেত্র	অর্থমিতি	অর্থমিতি
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1999	1990
প্রবর্তক	Marianne Baxter & Robert King	Cleveland, Cleveland, McRae & Terpenning
ধরন	Linear symmetric moving-average filter	nonparametric iterative smoother
মৌলিক উৎস	Baxter, M., & King, R. G. (1999). Measuring business cycles: Approximate band-pass filters for economic time series. Review of Economics and Statistics, 81(4), 575–593.	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	Baxter-King Filter, Band-Pass Filter (Baxter-King), BK Band-Pass Filter, Bant Geçiren Süzgeç	Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL)
সম্পর্কিত	3	3
সারসংক্ষেপ	The Baxter-King (BK) band-pass filter, introduced by Marianne Baxter and Robert King in 1999, is a linear symmetric moving-average filter designed to isolate cyclical fluctuations in macroeconomic time series that fall within a specified range of periodicities. It removes both very low-frequency trends and very high-frequency noise, retaining only the business-cycle component—typically oscillations with a period of six to thirty-two quarters for quarterly data.	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.