Compară metode

	DeepAR	Predicția conformă pentru prognoza seriilor de timp
Domeniu	Învățare profundă	Econometrie
Familie	Machine learning	Regression model
Anul apariției	2020	2021
Autorul original	Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)	Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)
Tip	Autoregressive recurrent neural network (probabilistic forecasting)	Distribution-free prediction interval wrapper
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.	Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591.
Denumiri alternative	DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR	conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi)
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.	Conformal prediction is a distribution-free wrapper that turns any point forecaster — ARIMA, a neural network, or a machine-learning model — into valid prediction intervals using only its residuals. The time-series form was popularised by Xu & Xie (2021) and the modern tutorial treatment by Angelopoulos & Bates (2023).