相关实验视频
卢比对美元的汇率预测使用深度神经网络与多输出滑动窗口方法
Ihsan Fathoni Amri1, Novia Yunanita1, Febi Anggun Lestari1
1Department of Data Science, Universitas Muhammadiyah Semarang, Jl. Kedungmundu Raya No.18, Semarang 50273, Indonesia.
MethodsX
|November 24, 2025
概括
本研究引入了一种深度学习模型,用于预测印尼卢比对美元的汇率,其表现优于传统方法. 门式循环单位 (GRU) 模型为财务决策提供准确和稳定的预测.
科学领域:
- *计算金融和计量经济学.
- * 深度学习在时间序列预测中的应用.
- * 宏观经济指标分析.
背景情况:
- *卢比美元汇率是印度尼西亚经济的一个关键指标,由于市场波动,复杂的动态和季节性,难以预测.
- *准确的汇率预测对于金融稳定和明智的经济决策至关重要.
研究的目的:
- * 开发和评估一种新的深度学习框架,用于同时预测卢比美元汇率的买入和卖出率.
- * 为了比较不同神经网络架构 (MLP,LSTM,GRU) 与统计基准 (VAR) 的性能.
主要方法:
- *采用多输出滑动窗深度学习框架,利用2015-2025年每日历史汇率数据.
- * 用数据规范化和正弦-弦时间编码来捕获每周周期性模式.
- * 评估了多层感知器 (MLP),长短期记忆 (LSTM) 和门式循环单元 (GRU) 模型,与矢量自回归 (VAR) 进行了基准测试.
主要成果:
- * 门式循环单位 (GRU) 模型表现出卓越的准确性和稳定性,特别是在高波动时期.
- *GRU以70:30的火车测试分割实现了强大的性能,获得了RMSE64.57,MAPE0.0031和R20.9875.2的RMSE.
- *该模型展示了可靠的短期预测能力,提前7天.
结论:
- * 提出的深度学习方法,特别是使用GRU,有效地解决了预测波动性卢比美元汇率的挑战.
- * 该框架能够同时预测买卖利率,这提高了其对金融机构和政策制定者的实际实用性.
- * 这项研究为在动态的货币市场中进行短期经济预测提供了强大的工具.
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