一种混合双分支模型,具有复发图和转换变压器,用于股票趋势预测
Jingyu Su1, Haoyu Li1, Ruiqi Wang1
1School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
Chaos (Woodbury, N.Y.)
|January 10, 2025
概括
本研究介绍了一种用于股票趋势预测的新型双分支网络,它结合了复发图和转换变压器. 该模型显著提高了预测股票价格波动的准确性.
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:
- 股票市场的预测是复杂的,因为固有的不确定性和非线性动态.
- 传统的方法往往难以捕捉金融时间序列数据中的复杂模式.
研究的目的:
- 开发一个先进的双分支网络模型,用于增强股票趋势预测.
- 提高预测每日股票价格变动的准确性和可靠性.
主要方法:
- 构建重复图 (RP) 来分析历史收盘价格的非线性关系.
- 使用转换变压器来识别多变量库存时间序列中的互连.
- 整合两个分支的特征,形成一个完全连接的二进制分类层.
主要成果:
- 拟议的双分支模型与传统的机器学习和深度学习方法相比,表现优越.
- 在七个选定的库存的历史数据上获得了更高的准确性 (ACC) 和F1分数.
- 验证了将复发图形与深度学习相结合的有效性,用于库存趋势预测.
结论:
- 双分支网络为股票趋势预测提供了一个强大的方法.
- 这种方法在改善财务决策和投资策略方面具有重大潜力.
- 将复发量化分析与深度学习相结合,可以增强对财务时间序列的分析.
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