一个增强的变压器框架,用于在线股票价格预测的增量学习
1Harvard extension school, Harvard University, Boston, Massachusetts, United States of America.
PloS one
|January 13, 2025
概括
本研究介绍了一种增量学习增强型变压器 (IL-ETransformer),用于实时股票价格预测. 该模型通过适应动态数据并捕捉复杂的时间和特征依赖性来提高准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 金融预测 金融预测
背景情况:
- 现有的股票预测模型在实时数据流中扎,显示可扩展性差,性能下降.
- 数据分布的动态变化和价格非静止性对准确的预测构成重大挑战.
研究的目的:
- 为在线股票价格预测提出基于增量学习的增强型变压器框架 (IL-ETransformer).
- 提高模型适应动态金融市场的能力,提高预测准确性.
主要方法:
- 利用多头自我注意机制来分析股票价格和特征因素之间的时间依赖.
- 采用持续规范化机制来稳定数据流.
- 使用时间序列弹性重量巩固 (TSEWC) 算法进行高效的增量训练.
主要成果:
- IL-ETransformer有效地捕获时间信息,并利用多维特征之间的相关性.
- 五个数据集的实验结果显示,股票价格预测准确度显著提高.
- 该方法以非静止且频繁变化的金融市场数据显示了强的性能.
结论:
- 拟议的IL-ETransformer框架为在线股票价格预测提供了一个可扩展和适应的解决方案.
- 它克服了传统模型在处理实时,动态财务数据方面的局限性.
- 该方法通过将增量学习与先进的变压器架构集成来提高预测准确性.
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