使用人工智能和时间序列模型预测实际汇率 (REER)
Moiz Qureshi1, Nawaz Ahmad2,3, Saif Ullah4
1Department of Statistics, Shaheed Benazir Bhutto University, Pakistan.
Heliyon
|May 30, 2023
概括
本研究将多层感知器 (MLP) 和极端学习机器 (ELM) 等机器学习 (ML) 模型与用于预测真实汇率数据 (REER) 的经典时间序列模型进行比较. 根据关键绩效指标 (KPI) 选择了表现最佳的模型.
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
- 金融建模金融建模
背景情况:
- 准确预测经济现象对于理解市场至关重要.
- 机器学习 (ML) 算法为分析复杂数据模式提供了先进的功能.
- 实际汇率 (REER) 数据是商业和金融市场的重要指标.
研究的目的:
- 用各种机器学习和时间序列模型来建模和预测实际汇率 (REER) 数据.
- 根据绩效标准,确定REER最有效的预测模型.
- 通过先进的分析技术,为了解汇率动态做出贡献.
主要方法:
- 使用的机器学习模型:多层感知器 (MLP) 和极端学习机器 (ELM).
- 采用经典时间序列模型:自回归集成移动平均 (ARIMA) 和指数平滑 (ES).
- 将模型应用于2019年1月至2022年6月的REER数据 (864个观察),分为培训和测试集.
主要成果:
- 所有模型都被应用并根据关键绩效指标 (KPI) 进行评估.
- 进行了比较分析,以确定模型的有效性.
- 这项研究确定了用于REER预测的表现最好的候选模型.
结论:
- 选择合适的预测模型对于准确的经济预测至关重要.
- 机器学习模型显示了捕获金融时间序列数据中复杂模式的潜力.
- 选择的模型提供了一个强大的方法来预测未来的实际汇率行为.
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