相关实验视频
一个MF-ConvLSTM-XAI模型,集成多功能和模糊控制,用于财务时间序列预测
Ruimin Liu1, Shuihan Yi2, Arman Ablikim3
1Université Paris-Dauphine, Paris, France.
Scientific reports
|November 29, 2025
概括
本研究引入了一种新的金融时间序列预测 (FTSF) 模型,MF-ConvLSTM-XAI,通过整合多特征数据和可解释的AI,显著提高了预测准确性和稳定性. 该模型显示了增强的性能,特别是在波动性市场.
科学领域:
- 金融预测 财务预测
- 人工智能的人工智能
- 时间序列分析时间序列分析
背景情况:
- 现有的金融时间序列预测方法与动态数据特征,非线性关系和整合多个特征作斗争,导致波动性市场的准确性差.
- 当前模型中缺乏可解释性,阻碍了对其决策过程的理解,降低了透明度和可信度.
研究的目的:
- 提出一种新的金融时间序列预测 (FTSF) 模型,即多特征卷积式长期内存网络-可解释的人工智能 (MF-ConvLSTM-XAI),以提高预测的准确性,稳定性和可解释性.
- 解决捕获动态特征,非线性关系和多特征集成在金融时间序列数据中的局限性.
主要方法:
- 该MF-ConvLSTM-XAI模型集成了卷积长期短期记忆网络 (ConvLSTM) 与可解释的人工智能 (XAI) 技术.
- 采用滑动窗技术进行数据分割,并使用格拉米安角差异场 (GADF) 将序列特征转换为图像格式.
- 将一个模糊控制机制 (FCM) 纳入ConvLSTM输入门,以增强信息流,并使用XAI进行特征重要性分析和决策路径可视化.
主要成果:
- 与传统的LSTM和基线方法相比,MF-ConvLSTM-XAI模型在纽约证券交易所综合指数数据集上实现了更高的预测准确性.
- 预测错误显著减少,平均平方误差 (MSE) 减少了约15%,平均绝对误差 (MAE) 减少了约12%.
- 展示了增强的稳定性和稳定性,特别是在高度波动的市场条件下.
结论:
- MF-ConvLSTM-XAI模型通过多功能集成和可解释的AI有效地提高了财务时间序列预测的准确性和稳定性.
- 该模型的可解释性提高了透明度和可信度,为金融数据分析提供了宝贵的见解.
- 建议对各种金融数据集进行进一步验证,以评估模型的概括能力.
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