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Time-series forecasting with deep learning: a survey

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Lim, Bryan, Zohren, Stefan·2021·Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
DOI 10.1098/rsta.2020.0209
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Abstract

Numerous deep learning architectures have been developed to accommodate the diversity of time-series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time-series forecasting—describing how temporal information is incorporated into predictions by each model. Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category. Lastly, we outline some ways in which deep learning can also facilitate decision support with time-series data. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

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Metadata

Authors
Lim, B., & Zohren, S.
Venue
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Publisher
The Royal Society
Published
2021-02-15
Volume
379
Issue
2194
Pages
20200209
ISSN
  • 1364-503X (ISSN-L)
  • 1471-2962
DOI
10.1098/rsta.2020.0209
OpenAlex
W3022643593
Cited by
1,787
References
23
FWCI
110.34
Type
journal article
Language
en
Open access
green
Licence
royalsociety.org/journals/ethics-policies/data-sharing-mining/ (tdm)

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CoChat. (2026). Citation check: Time-series forecasting with deep learning: a survey. https://app.cochat.ai/papers/10.1098/rsta.2020.0209
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Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. https://doi.org/10.1098/rsta.2020.0209

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