Comparar métodos
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| ARIMA estacional (SARIMA)× | STL Decomposition× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia≠ | Regression model | Process / pipeline |
| Año de origen≠ | 2015 | 1990 |
| Autor original≠ | Box & Jenkins (seasonal extension of ARIMA) | Cleveland, Cleveland, McRae & Terpenning |
| Tipo≠ | Seasonal time-series model | nonparametric iterative smoother |
| Fuente seminal≠ | Box, G.E.P., Jenkins, G.M., Reinsel, G.C. & Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | 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 ↗ |
| Alias≠ | seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA | Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL) |
| Relacionados≠ | 5 | 3 |
| Resumen≠ | SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period. | 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. |
| ScholarGateConjunto de datos ↗ |
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