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相关概念视频

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贝叶斯基因优化在自动化超参数调上的一种新方法.

Qi Li1, Norshaliza Kamaruddin2, Jia Zhang3

  • 1Faculty of Artificial Intelligence, UTM, Malaysia.

Scientific reports
|November 26, 2025
PubMed
概括

本研究介绍了一种基于贝叶斯的基因算法 (BayGA),用于优化股票市场预测模型. 这种新的方法提高了深度神经网络 (DNN) 的表现,显著超过主要股票指数.

关键词:
自动超参数调整调整贝叶斯语 贝叶斯语 贝叶斯语 贝叶斯语遗传算法 遗传算法 遗传算法多层感知器 (MLP); 长短记忆神经网络 (LSTM); 横截面股票回报预测.

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科学领域:

  • 计算金融是指计算金融.
  • 机器学习 机器学习
  • 金融预测 金融预测

背景情况:

  • 金融模型中的手动超参数调整可以降低预测准确度.
  • 深度神经网络 (DNN) 是有前途的,但需要仔细的参数优化.
  • 象征性遗传编程 (SGP) 为自动化模型开发提供了一个框架.

研究的目的:

  • 开发一种用于股票市场预测的自动化超参数调整方法,使用一种新的贝叶斯基基遗传算法 (BayGA).
  • 将BayGA与深度神经网络 (DNN) 框架集成,以进行增强的财务预测.
  • 评估与主要股票指数对比的拟议模型的预测性能.

主要方法:

  • 象征性遗传编程 (SGP) 与贝叶斯技术在深度神经网络 (DNN) 中的整合.
  • 开发和应用基于贝叶斯的基因算法 (BayGA) 来实现自动化超参数优化.
  • 对拟议的DNN-BayGA模型与基准股票指数 (HS300,CSI500,CSI1000) 的比较分析.

主要成果:

  • 与BayGA结合的DNN模型与主要股票指数相比表现优越.
  • 根据DNN-BayGA模型的年化回报率超过HS300的10.06%,CSI500的8.62%,CSI1000的16.42%.
  • 该模型实现了显著的卡尔马比率:HS300为3.83,CSI500为2.71,CSI1000为6.20.

结论:

  • 提出的贝叶斯基基因算法 (BayGA) 有效地优化了股票市场预测的超参数.
  • 集成的DNN-BayGA框架为财务预测提供了强大的高性能解决方案.
  • 这种方法显著提高了股票市场分析中的预测准确性和财务回报.