DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
This addresses the problem of optimizing business processes like inventory management for retail companies, representing a strong specific gain rather than an incremental improvement.
The paper tackled probabilistic forecasting for time series, such as demand in retail, by proposing DeepAR, an autoregressive recurrent network model, and achieved accuracy improvements of around 15% compared to state-of-the-art methods on real-world datasets.
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. We demonstrate how by applying deep learning techniques to forecasting, one can overcome many of the challenges faced by widely-used classical approaches to the problem. We show through extensive empirical evaluation on several real-world forecasting data sets accuracy improvements of around 15% compared to state-of-the-art methods.