阶段框架:基于图表注意力网络和稀疏的时空卷积网络的股票动态异常检测和趋势预测模型
Ming Shi1, Roznim Mohamad Rasli1, Shir Li Wang1
1Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia.
PloS one
|March 17, 2025
概括
阶段框架通过使用图表注意网络 (GAT),变化自编码器 (VAE) 和稀疏时空卷积网络 (STCN) 改进了库存预测和异常检测. 它在库存预测方面达到85%的准确性,在异常检测方面达到95%,超过现有方法.
科学领域:
- 金融技术是金融技术.
- 在金融领域的机器学习.
- 数据科学是数据科学.
背景情况:
- 金融市场日益复杂,需要先进的风险管理工具.
- 现有的库存预测和异常检测方法与复杂的库存间关系和数据异常作斗争.
研究的目的:
- 引入STAGE框架,用于增强库存预测和强大的异常检测.
- 为了解决当前金融数据分析技术的局限性.
主要方法:
- 将图表注意网络 (GAT),变化自编码器 (VAE) 和稀疏时空卷积网络 (STCN) 集成到 STAGE 框架中.
- 使用深度学习架构的组合进行时空分析和模式识别.
主要成果:
- 在20个时代之后,STAGE框架在库存预测中实现了85%的准确性,超过了其他模型的10-20%.
- 在异常检测中实现了95%的准确性,证明了快速的融合和稳定性.
- 该框架在处理复杂的金融市场动态方面证明是有效的.
结论:
- 在复杂的金融市场中,STAGE框架为股票预测和异常检测提供了创新和有效的解决方案.
- 综合方法提高了与现有方法相比的准确性和稳定性.
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