DeepAR: Probabilistic forecasting with autoregressive recurrent networks

David Salinas, Valentin Flunkert, Jan Gasthaus and Tim Januschowski

International Journal of Forecasting, 2020, vol. 36, issue 3, 1181-1191

Abstract: Probabilistic forecasting, i.e., estimating a time series’ future probability distribution given its past, is a key enabler for optimizing business processes. In retail businesses, for example, probabilistic demand forecasts are crucial for having the right inventory available at the right time and in the right place. This paper proposes DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an autoregressive recurrent neural network model on a large number of related time series. We demonstrate how the application of deep learning techniques to forecasting can overcome many of the challenges that are faced by widely-used classical approaches to the problem. By means of extensive empirical evaluations on several real-world forecasting datasets, we show that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.

Keywords: Probabilistic forecasting; Neural networks; Deep learning; Big data; Demand forecasting (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (117)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:3:p:1181-1191

DOI: 10.1016/j.ijforecast.2019.07.001

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