Compară metode

	DeepAR	Pădurea Aleatoare (Random Forest)
Domeniu	Învățare profundă	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2020	2001
Autorul original	Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)	Breiman, L.
Tip	Autoregressive recurrent neural network (probabilistic forecasting)	Ensemble (bagging of decision trees)
Sursa seminală	Salinas, D., Flunkert, V., Gasthaus, J. & Januschowski, T. (2020). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. International Journal of Forecasting, 36(3), 1181–1191.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Denumiri alternative	DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Înrudite	5	4
Rezumat	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.