LTR-Net:一种基于深度学习的方法,用于企业的财务数据预测和风险评估
1Changchun University of Finance and Economics, Changchun, Jilin, China.
PloS one
|August 1, 2025
概括
我们开发了LTR-Net,这是一个结合LSTM,Transformer和ResNet的深度学习模型,用于准确的财务数据预测和风险评估. 在金融数据集的准确性和稳定性方面,LTR-Net的表现优于现有的模型.
科学领域:
- 金融分析 金融分析
- 机器学习 机器学习
- 时间序列分析分析时间序列分析
背景情况:
- 财务数据预测和风险评估是复杂的多任务问题.
- 传统模型与时间依赖,全球信息和财务数据中的非线性关系作斗争.
- 预测准确度有限阻碍了有效的财务决策.
研究的目的:
- 提出LTR-Net,这是一种用于增强财务数据预测和风险评估的新型深度学习模型.
- 解决传统模型在捕捉复杂的金融数据动态方面的局限性.
- 提高财务预测的准确性,稳定性和稳定性.
主要方法:
- 开发了LTR-Net,这是一个集成LSTM,Transformer和ResNet的多模块深度学习架构.
- 集成的时间依赖模型,全球信息捕获和深度功能提取模块.
- 在Kaggle金融困境预测和雅虎金融股票市场数据集上评估了LTR-Net.
主要成果:
- 在金融数据集上,LTR-Net显著超过了LSTM,GRU,Transformer和DeepAR.
- 在MSE,RMSE,MAE和AUC等指标中实现了更高的准确性,稳定性和稳定性.
- 废弃研究证实了LSTM,变压器和ResNet模块的关键贡献.
结论:
- 对于金融数据预测和风险评估,LTR-Net提供了卓越的性能.
- 该模型展示了强大的概括能力,适用于其他数据分析领域.
- 在处理复杂的财务时间序列数据方面,LTR-Net代表了重大进步.
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