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’.
Keywords
Series (stratigraphy)Artificial intelligenceComputer scienceTime seriesMachine learningEconometricsMathematicsGeology
Affiliated Institutions
Related Publications
Deep learning for time series classification: a review
Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been...
Autoformer: Decomposition Transformers with Auto-Correlation for\n Long-Term Series Forecasting
Extending the forecasting time is a critical demand for real applications,\nsuch as extreme weather early warning and long-term energy consumption\nplanning. This paper studies ...
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands ...
Deep Learning on Graphs: A Survey
Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiq...
Object Detection With Deep Learning: A Review
Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection ...
Publication Info
- Year
- 2021
- Type
- article
- Volume
- 379
- Issue
- 2194
- Pages
- 20200209-20200209
- Citations
- 1366
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
1366
OpenAlex
Cite This
Bryan Lim,
Stefan Zohren
(2021).
Time-series forecasting with deep learning: a survey.
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
, 379
(2194)
, 20200209-20200209.
https://doi.org/10.1098/rsta.2020.0209
Identifiers
- DOI
- 10.1098/rsta.2020.0209