使用帕雷托阿尔法切割技术进行金价预测的多目标优化
1Assistant Professor, Sreenivasa Institute of Technology and Management Studies, India.
这项研究使用多目标优化框架和帕雷托阿尔法切割技术来增强黄金价格预测. 自动回归分布式滞后 (ARDL) 模型显示出财务决策的卓越准确性和稳定性.
科学领域:
- 数量金融
- 经济计量学
- 运营研究
背景情况:
- 准确预测黄金价格对于投资,采矿和财务规划至关重要.
- 现有的预测模型在平衡多个绩效指标方面面临挑战.
- 宏观经济因素显著影响黄金价格的动态.
研究的目的:
- 引入一个新的多目标优化框架来评估黄金价格预测模型.
- 通过管理准确性,波动性和合适性之间的权衡来增强模型选择.
- 使用帕雷托阿尔法切割技术确定帕雷托最佳预测模型.
主要方法:
- 使用自回归分布式滞后 (ARDL),随机和自回归集成移动平均 (ARIMA) 模型.
- 应用帕雷托最佳性原则与模糊逻辑相结合,用于多标准决策.
- 采用帕雷托α切割技术选基于RMSE,波动性和R平方的最佳模型.
主要成果:
- 自动回归分布式滞后 (ARDL) 模型始终显示出卓越的准确性和模型适应性.
- 随机模型在预测黄金价格方面表现出强的稳定性.
- 帕雷托阿尔法切割框架有效地确定了平衡准确性和稳定的模型.
结论:
- 拟议的框架为选择优质的黄金价格预测模型提供了强有力的方法.
- 这些发现为管理投资和商品风险的金融利益相关者提供了实用见解.
- 这种方法提高了对多个目标的预测模型性能的理解.
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