根据信息交互应用的金融投资风险预测火算法结合图形卷积网络
1Business school, University of New South Wales, Kensington, Sydney, Australia.
PloS one
|September 12, 2023
概括
这项研究通过改进的火算法 (FA) 优化的图形卷积网络 (GCN) 来增强金融投资风险预测. 优化的模型实现了91.9%的特征选择精度,超过了可靠投资决策的传统方法.
科学领域:
- 金融建模金融建模
- 机器学习应用程序 机器学习应用程序
- 风险管理 风险管理
背景情况:
- 预测金融投资风险对于决策至关重要.
- 传统模型往往缺乏准确性和稳定性.
- 图形卷积网络 (GCNs) 在复杂的数据分析中显示出潜力.
研究的目的:
- 开发一个优化的金融投资风险预测模型.
- 为了提高预测的准确性和可靠性.
- 为了利用图形卷积网络 (GCN) 和优化的火算法 (FA).
主要方法:
- 使用图形卷积网络 (GCN) 进行风险预测.
- 优化火算法 (FA) 以提高性能.
- 实验验证拟议模型的有效性.
主要成果:
- 实现了91.9%的最佳特征选择准确度.
- 经过大约30次代,证明了稳定的性能.
- 与传统模型相比,显示出明显更高的预测准确度,特别是在意想不到的事件中.
结论:
- 优化的GCN和FA模型为金融风险预测提供了高准确度和可靠性.
- 拟议的方法为金融投资决策提供了强有力的支持.
- 这项研究为优化金融风险预测模型提供了宝贵的见解.
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