一种可解释的深度学习方法用于股票市场趋势预测
Dost Muhammad1, Iftikhar Ahmed2, Khwaja Naveed3
1CRT-AI and ADAPT Research Centres, School of Computer Science, University of Galway, Ireland.
Heliyon
|November 21, 2024
概括
一个新的深度学习模型准确地预测股票市场的趋势,如上,下跌和图表模式,优于传统方法. 可解释的人工智能 (XAI) 提高了知情金融决策和风险管理的透明度.
科学领域:
- * 计算金融学
- * 机器学习 * 机器学习
- * 人工智能 * 人工智能
背景情况:
- * 股票市场预测涉及固有的风险和不确定性,需要强有力的趋势分析,以制定最佳的投资和撤资策略.
- *准确预测市场趋势对于利最大化和损失最小化至关重要.
研究的目的:
- * 提出一个深度学习模型来预测五种不同的股票市场趋势:上升,下降,双顶,圆底和圆顶.
- * 评估拟议模型的性能与常见的基准,如支持向量机,随机森林和后勤回归.
- *使用可解释AI (XAI) 技术来提高模型的解释性.
主要方法:
- *开发和实施一种用于股票趋势预测的新型深度学习模型.
- *与支持向量机器,随机森林和物流回归进行比较分析,使用四个不同的现实世界数据集.
- * 应用SHAP (夏普利添加式解释) 和LIME (局部可解释模型不可知解释) 来解释AI.
主要成果:
- * 深度学习模型的平均准确率为94.9%,明显超过随机森林 (85.7%),支持矢量机 (60.07%) 和后勤回归 (52.45%).
- * 该模型在94.85%的F1得分性能上表现出优异,与随机森林 (77.95%),支持矢量机 (21.02%) 和后勤回归 (46.23%) 相比.
- *SHAP分析确定了关键影响性特征,使潜在的特征减少,同时提高精度,回忆和F1得分,表明性能权衡.
结论:
- * 拟议的深度学习模型为股票市场趋势预测提供了强大而可解释的解决方案.
- *XAI技术的整合为预测驱动因素提供了有价值的见解,支持投资者决策.
- * 该模型的表现表明其在金融风险管理和战略投资规划方面的实际应用.
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