研究深度学习模型,通过集成频率域和时间序列特征来进行库存预测
Wenjie Sun1, Jianhua Mei2, Shengrui Liu3
1Department of Global Management, Seokyeong University, Seoul, 02173, Republic of Korea.
Scientific reports
|August 19, 2025
概括
本研究介绍了StockMixer与ATFNet,这是一个用于股票预测的新型深度学习模型. 它通过整合时间和频率领域特征来提高准确性,捕捉复杂的股票相互作用,以便更可靠的财务预测.
科学领域:
- 金融技术 (金融科技)
- 金融领域的机器学习
- 量化金融 量化金融
背景情况:
- 由于高波动性和不确定性,股票预测具有挑战性.
- 现有的模型往往无法捕捉多个尺度的时间模式,周期组件和股票间的相互作用.
- 这限制了传统股票价格预测模型的准确性和稳定性.
研究的目的:
- 开发一个先进的深度神经网络模型,以提高股票预测的准确性和可靠性.
- 整合时间域和频域特征,以便全面分析股票价格波动.
- 解决现有模型在捕捉复杂的时间模式和种群间关系方面的局限性.
主要方法:
- 拟议的StockMixer模型与自适应变压器融合网络 (ATFNet).
- 采用时间道混合模型 (MultTime2dMixer) 来解时间演变和道间相互作用.
- 介绍了一种非基于图形的股票关系建模方法 (NoGraphMixer),使用注意力机制来动态捕捉交叉股票依赖.
- 集成频域复杂注意模型 (ATFNet) 来捕捉时间和频域中的不连续性.
- 使用自适应融合方法,从时间和频率领域融合多式联络信息.
主要成果:
- 对预测价格运动方向的分类指标 (准确性,精度,回忆,F1分) 进行了显著改进.
- 在回报预测排名指标 (IC,RIC,Prec@N) 中提高性能.
- 通过更高的Sharpe比率 (SR) 显示出卓越的后台测试性能.
结论:
- 该StockMixer与ATFNet模型有效地整合了时间和频率域特征,以实现卓越的股票预测.
- 新型组件 (MultTime2dMixer,NoGraphMixer,ATFNet) 成功地解决了现有模型的局限性.
- 拟议的方法为金融技术库存预测提供了更准确,更稳定的解决方案.
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