一个新的金融危机预测的元启发式优化模型:迈向可持续发展
Mohamed Elhoseny1,2, Noura Metawa1,3, Ibrahim M El-Hasnony1
1Mansoura University, Egypt.
概括
这项研究引入了一种新的异常值检测模型,用于金融危机预测 (FCP),使用由政治优化器优化的深度神经网络. 新方法提高了对公司财务健康的预测准确度,这对经济稳定至关重要.
科学领域:
- * 经济学 * 经济学
- * 数据科学数据科学
- * 人工智能 * 人工智能
背景情况:
- * 全球危机凸显了需要强有力的金融危机预测 (FCP) 才能保持经济稳定.
- * 传统的FCP方法在准确性,数据合法性和超参数调整方面面临挑战.
- *机器学习 (ML) 和深度学习 (DL) 模型显示出希望,但需要提高培训效率.
研究的目的:
- * 为金融危机预测 (FCP) 开发一个先进的异常值检测模型.
- * 提高预测公司财务状况的准确性和效率.
- * 解决传统ML/DL模型的局限性,特别是超参数优化.
主要方法:
- * 提出了使用基于政治优化器的深度神经网络 (OD-PODNN) 的FCP异常检测模型.
- * 综合预处理,基于隔离森林 (iForest) 的异常值检测,以及PODNN分类模型.
- * 微调深度神经网络 (DNN) 超参数,以提高分类准确度.
主要成果:
- * OD-PODNN模型在金融危机预测方面表现优异.
- * 通过使用三个不同的数据集在各种绩效指标上进行评估.
- * 与现有的FCP方法相比,实现了更高的分类准确性.
结论:
- * OD-PODNN模型在金融危机预测方面取得了重大进展.
- * 集成异常检测和优化的DNN可以改善经济预测.
- *这种方法为评估企业财务健康提供了更可靠的工具.
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