AD-autoformer: decomposition transformers with attention distilling for long sequence time-series forecasting-Reference-Cited by-同舟云学术

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AD-autoformer: decomposition transformers with attention distilling for long sequence time-series forecasting

Published:2024-06-06 Issue:14 Volume:80 Page:21128-21148
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Cao Danyang,Zhang Shuai

Funder

Ministry of Education, Humanities and social science research projects

Yuyou Talent Support Plan of North China University of Technology

2024 Graduate Education Reform Project of North China University of Technology

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11227-024-06266-8.pdf

Reference44 articles.

1. Liu L, Shen J, Tang J, et al. (2018) Learning the joint representation of heterogeneous temporal events for clinical endpoint prediction. arXiv https://arxiv.org/abs/1803.04837

2. Cao W, Hu L, Cao L, et al. (2015) Deep modeling complex couplings within financial markets. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. ACM, pp 2518–2524

3. Hulot P, Aloise D, Jena S D, et al. (2018) Towards station-level demand prediction for effective rebalancing in bike-sharing systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, pp 378–386

4. Du Y, Wang W, Wang L, et al. (2015) Hierarchical recurrent neural network for skeleton based action recognition. In: Computer Vision and Pattern Recognition, pp 1110–1118

5. Ai Y, Li Z, Gan M et al (2019) A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system. Neural Comput Appl 31(5):1665–1677

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