方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 季节性ARIMA（SARIMA）× | STL分解：使用Loess的季节-趋势分解× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 2015 | 1990 |
| 提出者≠ | Box & Jenkins (seasonal extension of ARIMA) | Cleveland, Cleveland, McRae & Terpenning |
| 类型≠ | Seasonal time-series model | nonparametric iterative smoother |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA | Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL) |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|