对于财务预测而言,深度上下文关注的变压器转移学习

Ling Feng¹, Ananta Sinchai¹

¹School of Integrated Innovative Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand.

PeerJ. Computer science

|September 24, 2025

概括

本研究介绍了2CAT,这是一种用于财务时间序列预测的深度学习模型. 它在各种股票市场实现了卓越的预测准确性,证明了有效的跨市场知识转移.

科学领域:

计算金融是一种计算金融.
机器学习用于时间序列.
金融市场的深度学习

背景情况:

财务时间序列预测对于投资决策至关重要.
现有的深度学习模型在捕捉复杂的时间动态和跨市场的概括方面面临挑战.
对于能够适应不同市场条件的强大模型的需求至关重要.

研究的目的:

开发和评估2CAT (CNN相关联式注意力转换器),这是一种用于财务时间序列预测的新型深度学习模型.
评估2CAT集成架构的有效性,包括信号分解,卷积层和基于相关性的注意.
调查转移学习框架的好处,以加强跨市场通用化.

主要方法:

开发了2CAT,整合了CNN,基于关联的注意力和变压器.
采用转移学习框架,包括预训练,编码器结和微调.
在六个主要股票指数上评估模型:DJIA,N225,HSI,SSE,BSE和SET.
进行了废除研究和预测地平线分析.

主要成果:

与深度变压器相比,2CAT在道斯工业平均指数 (DJIA) 上表现出更高的预测准确度 (例如,R2 的 0.9169 与 0.8274).
在泰国证券交易所 (SET) 指数 (R2为0.9094) 上取得了显著的改善,此前这个市场对预测模型具有挑战性.
威尔科克森的签名级别测试证实了在非转移和转移学习场景 (p <0.05) 中,在统计学上显著的绩效增长.
转移学习实验验证实了跨市场知识转移的可行性和有效性.

结论:

2CAT是一种强大的,可适应的深度学习框架,用于财务时间序列预测.
该模型的架构有效地捕捉了时间模式,并在不同的金融市场上很好地概括.
转移学习显著提高预测性能和适应各种市场条件的适应性.

关键词:

语境学习是一种学习.功能提取功能提取知识转移知识转移.时间预测时间预测.基于变压器的模型

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