DRCNN: decomposing residual convolutional neural networks for time series forecasting

Recent studies have shown great performance of Transformer-based models in long-term time series forecasting due to their ability in capturing long-term dependencies. However, Transformers have their limitations when training on small datasets because of their lack in necessary inductive bias for ti...

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Detalles Bibliográficos
Autores principales: Zhu, Yuzhen, Luo, Shaojie, Huang, Di, Zheng, Weiyan, Su, Fang, Hou, Beiping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517921/
https://www.ncbi.nlm.nih.gov/pubmed/37741848
http://dx.doi.org/10.1038/s41598-023-42815-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517921/
https://www.ncbi.nlm.nih.gov/pubmed/37741848
http://dx.doi.org/10.1038/s41598-023-42815-6

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