预测股票价格使用长期短期记忆涉及注意力方法:证券交易行业的应用
Muhammad Idrees1, Maqbool Hussain Sial1, Najam Ul Hassan2
1Department of Economics and Quantitative Methods, Dr. Hasan Murad School of Management (HSM), University of Management and Technology (UMT), Lahore, Pakistan.
PloS one
|March 18, 2025
概括
这项研究使用卡拉奇证券交易所数据开发了四种深度学习模型来预测股票价格. 长期短期记忆与注意力模型获得了最高的准确性,证明了其在财务预测中的有效性.
科学领域:
- * 金融市场和计量经济学
- * 计算智能和机器学习
背景情况:
- *经济稳定是一个全球性的挑战,特别是在发展中国家.
- *准确的股票市场预测对经济稳定至关重要,几十年来一直是研究的重点.
- *已经开发了各种预测模型,并正在努力提高其有效性.
研究的目的:
- *为股票价格预测量身定制和评估四种不同的深度学习模型.
- * 评估人工神经网络,重复性神经网络与注意力,长期短期记忆网络与注意力,以及有门的重复性单元与注意力的性能.
- * 确定最有效的模型来预测股票交易所价格.
主要方法:
- *利用了卡拉奇证券交易所 (100指数) 从2008年2月22日到2021年2月23日的每日业务数据.
- * 实现并比较了四种深度学习架构:人工神经网络 (ANN),重复性神经网络 (RNN) 与注意力,长期短期记忆网络 (LSTM) 与注意力,以及有门的重复性单元 (GRU) 与注意力.
- *使用收集的历史库存数据训练和测试模型.
主要成果:
- * 长期短期记忆网络与注意力模型在股票价格预测中表现出卓越的表现.
- *达到高的R平方值:0.9996 (培训),0.9980 (验证) 和0.9921 (测试).
- * 在预测准确度方面表现优于ANN,RNN与注意力,GRU与注意力模型.
结论:
- * 深度学习模型,特别是带有注意力的LSTM,为股票价格预测提供了强大的方法.
- * 拟议的LSTM与注意力模型证明了金融市场分析的高准确性和可靠性.
- * 这项研究有助于通过先进的预测建模来加强股票市场稳定性的持续努力.
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