使用变化模式分解进行多分辨率研磨机因果关系测试:一个python软件
Foued Saâdaoui1,2, Hana Rabbouch2,3
1Rabat Business School, International University of Rabat, Sala-Al-Jadida, Morocco.
Journal of applied statistics
|December 10, 2025
概括
本研究引入了使用变量模式分解 (VMD) 分析复杂时间序列数据的多尺度格兰杰因果关系测试. 该方法增强了在不同频率尺度上的因果发现,以提高金融,工程和医学方面的准确性.
科学领域:
- 时间序列分析时间序列分析
- 因果关系 推断 推理
- 信号处理 信号处理
背景情况:
- 传统的格兰杰因果关系测试可以忽略聚合时间序列数据中的复杂相互作用.
- 在多个频率尺度上分析因果关系对于理解复杂系统动态至关重要.
研究的目的:
- 为格兰杰因果关系测试开发一种先进的多尺度方法.
- 提高因果关系分析的精度和细节性.
- 为复杂的数据分析提供强大且易于使用的工具.
主要方法:
- 变化模式分解 (VMD) 与传统的格兰杰因果关系测试的整合.
- 时间序列分解为代表不同的频率尺度的内在模式函数 (IMF).
- 将格兰杰因果关系测试应用于静态IMF,以详细识别因果关系模式.
主要成果:
- 多尺度方法有效地揭示了隐藏在聚合数据中的因果关系模式.
- 关于加密货币,生物医学信号和模拟的实证研究证实了该方法的有效性.
- 与VMD集成的格兰杰因果关系与现有技术相比,显示出更高的准确性和精度.
结论:
- 基于VMD的新型多尺度格兰杰因果测试为因果分析提供了灵活而强大的框架.
- 这种方法显著提高了揭示复杂系统中隐藏的因果相互作用的能力.
- 易于使用的Python软件包增强了各种科学领域的研究人员和从业人员的可访问性.
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