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DeepAR: Probabilistic forecasting with autoregressive recurrent networks

Open access
Salinas, David, Flunkert, Valentin, Gasthaus, Jan, Januschowski, Tim·2020·International Journal of Forecasting
DOI 10.1016/j.ijforecast.2019.07.001
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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.

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Research works printed but not linked: 25

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Metadata

Authors
Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T.
Venue
International Journal of Forecasting
Publisher
Elsevier BV
Published
2020-07
Volume
36
Issue
3
Pages
1181-1191
ISSN
0169-2070 (ISSN-L)
DOI
10.1016/j.ijforecast.2019.07.001
OpenAlex
W2607045400
Cited by
2,661
References
6
FWCI
106.60
Type
journal article
Language
en
Update notices
erratum (2021-07-01)
Open access
hybrid
Licence

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CoChat. (2026). Citation check: DeepAR: Probabilistic forecasting with autoregressive recurrent networks. https://app.cochat.ai/papers/10.1016/j.ijforecast.2019.07.001
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Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2019.07.001

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.