基于高阶混合集群算法设计的智能金融数据管理系统
1School of Management, Wuhan Technology And Business University, Wuhan, China.
PeerJ. Computer science
|December 13, 2024
概括
本研究介绍了ATT-VAE,这是一种用于预测金融风险的AI模型. 它增强了带有注意力机制的变化自动编码器,改善了财务风险预测和数据治理.
科学领域:
- 人工智能的人工智能
- 金融数据科学 数据科学
- 机器学习 机器学习
背景情况:
- 越来越多的金融数据需要先进的风险预测方法.
- 目前的金融风险管理需要智能系统,以谨慎处理数据.
- 用于预测金融风险的系统设计是研究的一个关键领域.
研究的目的:
- 开发和展示一个智能金融风险预测模型.
- 设计一个利用人工智能来预测公司财务风险的系统.
- 通过先进的人工智能技术来增强财务数据管理.
主要方法:
- 一个新的数据模型,ATT-VAE,基于带有注意力机制的变化自编码器 (VAE).
- 使用VAE.编码和增强多维财务数据.
- 采用注意力机制来丰富VAE输出,以改善聚类.
主要成果:
- 该ATT-VAE模型在各种公共和地方金融数据集上表现出卓越的表现.
- 实现了超过0.7的集群精度指数,超过了现有的深度集群网络.
- 使用多式联运数据集 (AWA,CUB) 和当地金融数据,验证了有效性.
结论:
- 该ATT-VAE模型为智能金融风险预测提供了一个强大的框架.
- 拟议的方法为未来的金融数据治理提供了算法基础.
- 这项研究是人工智能驱动的金融审查的关键参考.
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