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RCSAN剩余增强通道空间注意力网络用于股票价格预测
WenJie Sun1, Ziyang Liu2, ChunHong Yuan3
1Department of Global Management, Seokyeong University, Seoul, 027028, South Korea.
Scientific reports
|July 2, 2025
概括
本研究介绍了剩余增强的通道空间注意网络 (R-CSAN),用于准确的股价预测. R-CSAN显著优于现有模型,提供了改进的金融时间序列分析和交易策略洞察力.
科学领域:
- 人工智能的人工智能
- 金融预测 金融预测
- 机器学习 机器学习
背景情况:
- 金融时间序列数据呈现出复杂的多维模式.
- 准确的股价预测对于投资和交易策略至关重要.
- 现有的模型往往难以捕捉复杂的时间和空间依赖.
研究的目的:
- 开发一个先进的深度学习模型,用于增强股价预测.
- 为了有效地捕捉财务时间序列数据中的多维模式.
- 提高股票市场预测的准确性和稳定性.
主要方法:
- 提出了剩余增强的通道空间注意网络 (R-CSAN).
- 使用编码器-解码器架构,具有通道空间注意力和剩余连接.
- 包含掩盖和交叉注意机制,以防止信息泄露,并模拟市场间的相关性.
主要成果:
- R-CSAN显著优于传统 (ARIMA,LSTM) 和基于变压器的模型 (Informer,Autoformer).
- 实现了RMSE的大幅降低 (17.3-49.3%与传统相比,6.2-11.6%与变压器相比).
- 证明了高精度 ([公式:见文本]高达93.17%) 和投资回报率 (482.64%) 的提高.
结论:
- R-CSAN提供了一种强大而准确的股票价格预测方法.
- 模型的注意力机制和架构对性能至关重要.
- 为定量交易策略和投资组合优化提供了宝贵的见解.
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