Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| DeepAR× | Random Forest× | |
|---|---|---|
| Campo≠ | Aprendizaje profundo | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2020 | 2001 |
| Autor original≠ | Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon) | Breiman, L. |
| Tipo≠ | Autoregressive recurrent neural network (probabilistic forecasting) | Ensemble (bagging of decision trees) |
| Fuente seminal≠ | Salinas, D., Flunkert, V., Gasthaus, J. & Januschowski, T. (2020). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. International Journal of Forecasting, 36(3), 1181–1191. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias≠ | DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | DeepAR is Amazon's industrial forecasting model, introduced by Salinas, Flunkert and Gasthaus (2017; published 2020), that uses an autoregressive recurrent neural network to estimate the parameters of a probability distribution at each step, producing a confidence interval rather than a single point forecast. It can model many related time series jointly within one model. | 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. |
| ScholarGateConjunto de datos ↗ |
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