Title: DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks,
Abstract: A key enabler for optimizing business processes is accurately estimating the
probability distribution of a time series future given its past. Such
probabilistic forecasts are crucial for example for reducing excess inventory
in supply chains. In this paper we propose DeepAR, a novel methodology for
producing accurate probabilistic forecasts, based on training an
auto-regressive recurrent network model on a large number of related time
series. We show through extensive empirical evaluation on several real-world
forecasting data sets that our methodology is more accurate than
state-of-the-art models, while requiring minimal feature engineering.