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| Modèle ARIMA (Autoregressive Integrated Moving Average)× | Forêt Aléatoire× | |
|---|---|---|
| Domaine≠ | Économétrie | Apprentissage automatique |
| Famille≠ | Regression model | Machine learning |
| Année d'origine≠ | 2015 | 2001 |
| Auteur d'origine≠ | Box & Jenkins (Box-Jenkins methodology) | Breiman, L. |
| Type≠ | Univariate time-series model | Ensemble (bagging of decision trees) |
| Source fondatrice≠ | 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 | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | 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). | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateJeu de données ↗ |
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