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| Décomposition STL : Décomposition Saisonnier-Tendance par Loess× | Modèle ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille≠ | Process / pipeline | Regression model |
| Année d'origine≠ | 1990 | 2015 |
| Auteur d'origine≠ | Cleveland, Cleveland, McRae & Terpenning | Box & Jenkins (Box-Jenkins methodology) |
| Type≠ | nonparametric iterative smoother | Univariate time-series model |
| Source fondatrice≠ | 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 |
| Alias≠ | Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Apparentées≠ | 3 | 5 |
| Résumé≠ | 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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