Improving stock trend prediction with pretrain multi-granularity denoising contrastive learning-Reference-Cited by-同舟云学术

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Improving stock trend prediction with pretrain multi-granularity denoising contrastive learning

Published:2023-12-28 Issue: Volume: Page:
ISSN:0219-1377
Container-title:Knowledge and Information Systems
language:en
Short-container-title:Knowl Inf Syst

Author:

Wang Mingjie,Wang Siyuan,Guo Jianxiong,Jia Weijia

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s10115-023-02006-1.pdf

Reference58 articles.

1. Wang M, Chen F, Guo J, Jia W (2023) Improving stock trend prediction with multi-granularity denoising contrastive learning. In: International joint conference on neural networks (IJCNN). https://doi.org/10.1109/IJCNN54540.2023.10191523

2. Jiang W (2021) Applications of deep learning in stock market prediction: recent progress. Expert Syst Appl 184:115537

3. Chen Y, Wei Z, Huang X (2018) Incorporating corporation relationship via graph convolutional neural networks for stock price prediction. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 1655–1658

4. Keogh E, Lin J (2005) Clustering of time-series subsequences is meaningless: implications for previous and future research. Knowl Inf Syst 8:154–177

5. Chen C, Zhao L, Bian J, Xing C, Liu T-Y (2019) Investment behaviors can tell what inside: exploring stock intrinsic properties for stock trend prediction. In: Proceedings of the 25th ACM SIGKDD, pp 2376–2384

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