- Published
- 2021-02-15
- Venue
- Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, vol. 379, no. 2194, pp. 20200209
- Cited by
- 1,837
- Open access
- Yes, green
- DOI
- 10.1098/rsta.2020.0209
- PubMed
- 33583273
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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