预测股票市场指数使用编码器,解码器和注意力机制的整合
1Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam.
Entropy (Basel, Switzerland)
|January 24, 2025
概括
本研究介绍了股票市场预测的先进框架,通过集成编码器-解码器架构与注意力机制和贝叶斯优化来提高预测准确性. 这种新的方法在捕获复杂的财务数据模式方面明显优于传统方法.
科学领域:
- 计算金融是指计算金融.
- 机器学习 机器学习
- 时间序列分析时间序列分析
背景情况:
- 准确的股票市场指数预测对于财务决策至关重要.
- 传统的方法经常在金融数据中与复杂的时间依赖性作斗争.
研究的目的:
- 开发一种用于增强股票市场指数预测的新框架.
- 利用编码器-解码器架构,注意力机制和贝叶斯优化来提高预测准确性.
主要方法:
- 利用编码器-解码器架构来处理连续的股票价格数据.
- 集成了一个注意力机制,专注于相关的输入序列段.
- 采用贝叶斯优化进行超参数调整,以最大限度地提高预测精度.
主要成果:
- 拟议的框架显示了预测精度的显著改进.
- 与传统的循环神经网络相比,实现了更高的性能.
- 有效地捕捉到股票价格数据中的复杂模式和依赖关系.
结论:
- 综合框架显示了准确的股票市场预测的巨大潜力.
- 先进的神经网络架构和优化技术的结合是有效的.
- 这种方法为分析复杂的金融时间序列提供了强大的解决方案.
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