使用金融会计信息系统优化利预测优化通过优化DLSTM.
Wei Tang1,2, Shuili Yang1, Mohammad Khishe3
1School of Economics and Management, Xi'an University of Technology, Xi'an, 710054, Shaanxi, China.
Heliyon
|October 9, 2023
概括
这项研究引入了一种新的方法,将深度长期短期记忆 (DLSTM) 与双重可调强化黑猩猩优化算法 (TAR-CHOA) 结合起来,以改进财务会计利预测. DLSTM-TAR-CHOA模型在使用复杂的金融数据集预测利方面表现出卓越的表现.
科学领域:
- 财务会计信息系统 (FAIS) 是一种金融会计信息系统.
- 计算智能是一种计算智能.
- 机器学习 机器学习
背景情况:
- 深度学习 (DL) 和基于群体的算法在FAIS中越来越多地使用.
- 庞大的数据集的高度复杂性给混合网络在财务预测中带来了挑战.
研究的目的:
- 开发用于财务会计利预测的先进方法.
- 用混合DL和优化算法提高利预测模型的有效性.
主要方法:
- 结合双调强化黑猩猩优化算法 (TAR-CHOA) 与深度长期短期记忆 (DLSTM).
- 开发一个新的数据集,使用来自中国股票市场Kaggle数据集的15个输入.
- 设计和评估五个基于DLSTM的优化算法,用于利预测.
主要成果:
- 在评估的基于DL的金融会计利预测模型中,DLSTM-TAR-CHOA模型实现了最高的性能.
- 新开发的TAR-CHOA算法显著提高了利预测模型的有效性.
结论:
- DLSTM-TAR-CHOA模型代表了财务会计利预测的最先进方法.
- 未来的研究应该探索替代方法来解决数据依赖性和市场变化.
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