DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

Understand

Probabilistic forecasting, i.e.

• estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes.
• In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place.
• In this paper we propose DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an auto regressive recurrent network model on a large number of related time series.