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	DeepAR	Modello ARIMA (Autoregressive Integrated Moving Average)	Predizione conforme per previsioni di serie temporali
Campo	Apprendimento profondo	Econometria	Econometria
Famiglia	Machine learning	Regression model	Regression model
Anno di origine	2020	2015	2021
Ideatore	Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)	Box & Jenkins (Box-Jenkins methodology)	Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)
Tipo	Autoregressive recurrent neural network (probabilistic forecasting)	Univariate time-series model	Distribution-free prediction interval wrapper
Fonte seminale	Salinas, D., Flunkert, V., Gasthaus, J. & Januschowski, T. (2020). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. International Journal of Forecasting, 36(3), 1181–1191.	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	Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591.
Alias	DeepAR — Olasılıksal RNN Tahmini, probabilistic autoregressive RNN forecasting, Amazon DeepAR	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi)
Correlati	5	5	4
Sintesi	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.	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).	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).