周期聚合变压器用于学习长时间金融时间序列中的潜在季节性
Zhenyang Tang1, Jinshui Huang1, Denisa Rinprasertmeechai1
1Southwestern University of Finance and Economics, Chengdu, China.
PloS one
|August 8, 2024
概括
本研究介绍了用于金融市场预测的潜期聚合股票转换器 (LPAST). 这种新型模型通过捕捉股票数据中的复杂时间模式来改善长期预测.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 时间序列分析时间序列分析
背景情况:
- 金融市场表现出复杂的动态,由许多因素驱动.
- 像RNN和LSTM这样的传统模型捕捉短期依赖性,但错过了长期模式.
- 现有的方法与金融时间序列中的复杂,不可观察的时间结构作斗争.
研究的目的:
- 为增强金融时间序列预测提出一个适应期积累模型.
- 解决固定时间窗口方法在捕捉复杂的市场动态方面的局限性.
- 在长期财务预测任务中提高多步预测准确度.
主要方法:
- 开发了潜周期聚合库存转换器 (LPAST) 模型.
- 集成了一个变化自编码器 (VAE) 与一个周期对周期的注意力机制.
- 引入了自我相关性学习和用于多期聚合的路由机制.
主要成果:
- 提出了一种新的财务数据周期聚合表示方案.
- 引入了一个针对金融时间序列量身定制的新关注机制.
- 与现有方法相比,LPAST在长期预测任务中表现出优异的性能.
结论:
- LPAST模型有效地捕捉了金融市场中复杂的时间模式.
- 提出的方法在金融市场预测方面显示出显著的潜力和有效性.
- 突出了金融研究和预测分析应用的相关性.
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