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| STL 분해: Loess를 이용한 계절-추세 분해× | LOESS / LOWESS 지역 회귀× | |
|---|---|---|
| 분야≠ | 계량경제학 | 머신러닝 |
| 계열≠ | Process / pipeline | Machine learning |
| 기원 연도≠ | 1990 | 1979 |
| 창시자≠ | Cleveland, Cleveland, McRae & Terpenning | William S. Cleveland |
| 유형≠ | nonparametric iterative smoother | Local nonparametric regression smoother |
| 원전≠ | 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. link ↗ | Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836. DOI ↗ |
| 별칭 | Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL) | LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon |
| 관련 | 3 | 3 |
| 요약≠ | 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. |
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