在可持续发展的背景下,基于改进的ABC-RNN+Bi-LSTM算法设计了一种企业金融危机预测模型
1School of Management, Wuhan University of Bioengineering, Wuhan, Hubei, China.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了一种人工蜂群循环神经网络 (ABC-RNN) 和双向长期短期记忆 (Bi-LSTM) 模型,用于预测金融危机. 该模型实现了高精度,帮助企业及时预防危机,以实现可持续的经济发展.
科学领域:
- 金融风险管理 金融风险管理
- 金融领域的人工智能
- 可持续经济发展 可持续经济发展
背景情况:
- 经济全球化为公司带来了机遇和挑战,增加了对严峻外部环境的暴露.
- 管理人员专注于资本市场投资可能会导致决策错误和金融危机,因为追求过度的利.
研究的目的:
- 提出一种新的金融危机预测模型.
- 使用先进的人工智能技术,提高金融危机预测的准确性和速度.
主要方法:
- 开发一种金融危机预测模型,结合人工蜂群复发神经网络 (ABC-RNN) 和双向长期短期记忆 (Bi-LSTM).
- 整合一个特有的注意力机制,从财务数据中提取深层特征.
- 使用ABC算法优化RNN权重和偏差,以提高推理速度和准确性.
主要成果:
- 拟议的ABC-RNN与Bi-LSTM和注意力机制展示了强大的预测能力.
- 在测试组件上实现了88.94%的预测准确度和88.23%的回忆.
- 该模型有效地从财务数据中提取时间和深度特征.
结论:
- 开发的模型为预测企业金融危机提供了一个强大的解决方案.
- 早期发现金融困境可以及时干预,减轻负面影响.
- 这项研究通过提高企业的财务弹性来支持市场经济的可持续性.
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