使用生成对抗网络预测冈比亚的GDP
Haruna Jallow1, Alieu Gibba2, Ronald Waweru Mwangi3
1Department of Mathematics (Data Science Option), Pan African University Institute for Basic Sciences, Technology and Innovation, Kiambu, Kenya.
Frontiers in artificial intelligence
|March 20, 2025
概括
本研究使用生成对抗网络 (GAN) 预测国内生产总值 (GDP),达到99%的准确性. GAN模型优于其他经济增长预测方法,为决策者提供了有价值的见解.
科学领域:
- 经济学 经济学 经济学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 准确的国内生产总值 (GDP) 预测对于国家经济分析和增长至关重要.
- 传统的预测方法可能会与复杂的经济相互依赖和有限的数据场景作斗争.
- 冈比亚的经济为先进的预测建模提供了一个案例研究,因为它具有特殊的特点.
研究的目的:
- 使用包括政府支出,通货膨胀,援助,汇款和外国直接投资在内的关键经济指标预测GDP.
- 评估生成对抗网络 (GAN) 作为一种深度学习方法,以提高GDP预测的准确性.
- 为了证明GAN在小数据环境中的有效性,用于经济预测.
主要方法:
- 实现GDP预测的生成对抗网络 (GAN).
- 利用政府支出,通货膨胀,官方发展援助,汇款流入和外国直接投资 (FDI) 等经济因素.
- 与随机森林回归 (RF),XGBoost (XGB) 和支持矢量回归 (SVR) 模型进行比较分析.
主要成果:
- 生成对抗网络 (GAN) 实现了最高的预测准确率,达到99%.
- 射频和XGBoost模型也表现出强的性能,准确度为98%.
- GAN在捕捉GDP与其影响经济因素之间的复杂相关性方面表现出卓越的表现.
结论:
- 生成对抗网络 (GAN) 为GDP预测提供了一个高度准确和理想的方法,特别是在数据有限的经济体.
- 该研究为决策者和利益相关者提供了宝贵的工具,帮助他们制定可持续经济增长的战略.
- 从先进的机器学习技术中获得准确的GDP预测可以支持明智的经济决策.
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