The DOI matches its Crossref record; 127 of the 127 references it cites were found in a registry or an index.
Deep Learning for Time Series Forecasting: A Survey
Abstract
Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyper-parameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.
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Cites
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Cited by
Transformers in Time Series: A Survey
Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., & Sun, L. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/759 · Cited by 1,260
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Hannan, M. A., How, D. N. T., Lipu, M. S. H., Mansor, M., Ker, P. J., Dong, Z. Y., Sahari, K. S. M., Tiong, S. K., Muttaqi, K. M., Mahlia, T. M. I., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-98915-8 · Cited by 196
Convolutional neural network and long short-term memory models for ice-jam predictions
Madaeni, F., Chokmani, K., Lhissou, R., Homayouni, S., Gauthier, Y., & Tolszczuk-Leclerc, S. (2022) · The Cryosphere · doi:10.5194/tc-16-1447-2022 · Cited by 51
Modern Machine Learning Methods for Time Series Analysis
Huang, C., & Petukhina, A. (2022) · Statistics and Computing · doi:10.1007/978-3-031-13584-2_10 · Cited by 2
Metadata
- Authors
- Torres, J. F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., & Troncoso, A.
- Venue
- Big Data
- Publisher
- SAGE Publications
- Published
- 2021-02-01
- Volume
- 9
- Issue
- 1
- Pages
- 3-21
- ISSN
- 2167-6461 (ISSN-L)
- 2167-647X
- OpenAlex
- W3109365969
- Cited by
- 818
- References
- 127
- FWCI
- 40.94
- Type
- journal article
- Language
- en
- Open access
- closed
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