手法を比較
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| STL分解：loessを用いた季節・トレンド分解× | ARIMA（自己回帰和分移動平均）モデル× | |
|---|---|---|
| 分野 | 計量経済学 | 計量経済学 |
| 系統≠ | Process / pipeline | Regression model |
| 提唱年≠ | 1990 | 2015 |
| 提唱者≠ | Cleveland, Cleveland, McRae & Terpenning | Box & Jenkins (Box-Jenkins methodology) |
| 種類≠ | nonparametric iterative smoother | Univariate time-series model |
| 原典≠ | 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 ↗ | 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 |
| 別名≠ | Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| 関連≠ | 3 | 5 |
| 概要≠ | 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. | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
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