发展中国家建筑成本的宏观经济意识预测:使用封闭的循环单元和长期短期记忆深度学习框架
Majed Alzara1, Nadeen Gihad2, Heba Abdou3
1Department of Civil Engineering, College of Engineering, Jouf University, Sakaka, Saudi Arabia.
PloS one
|October 9, 2025
概括
本研究使用深度学习模型 (LSTM和GRU) 为埃及开发了本地化建筑成本指数 (CCI). 这些模型准确地预测了建筑成本,石油价格被认为是关键因素,有助于埃及建筑业的财务规划.
科学领域:
- 建筑经济学 建筑经济学
- 计量经济学 计量经济学 计量经济学
- 数据科学数据科学数据科学
背景情况:
- 由于不准确的估计和经济波动,成本超支困扰着长期建筑项目.
- 材料成本 (钢铁,水泥等) 的成本. 这将显著影响埃及的建筑费用,受经济和金融市场的影响.
- 埃及缺乏官方的建筑成本指数 (CCI),这阻碍了精确的财务规划和风险管理.
研究的目的:
- 开发一个针对埃及建筑业量身定制的本地化建筑成本指数 (CCI) 的预测模型.
- 通过准确的成本预测,加强财务规划并减轻埃及建筑业内的风险.
- 为应对新兴市场本地化预测模型的需求.
主要方法:
- 利用长期短期记忆 (LSTM) 和门式循环单元 (GRU) 深度学习模型进行CCI预测.
- 包括各种指标:宏观经济,货币,外汇,商品/能源,股票市场和技术指标.
- 在Python中使用先进的统计方法 (相关性分析,多对线性,逐步回归) 进行特征选择.
主要成果:
- 在校准和绝对误差指标方面,GRU模型表现出色;在平方损失和转折点预测方面,LSTM模型表现更好.
- 转换特征重要性分析确定石油是影响埃及CCI的最重要因素.
- 深度学习模型在预测与建筑成本相关的经济指标方面表现出很高的准确性.
结论:
- 开发的深度学习框架为埃及建筑业提供了强大的数据驱动工具.
- 准确的,本地化的CCI预测可以显著改善成本估计和财务规划.
- 这项研究证实了深度学习对建筑业经济指标预测的有效性.
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