在混合频时间序列之间推断定向的光谱信息流.
Qiqi Xian1,2, Zhe Sage Chen1,3,4
1Department of Psychiatry, Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA.
Research square
|March 10, 2025
概括
这项研究引入了一种新方法,即混合频时间频率正规关联分析 (MF-TFCCA),用于准确地测量复杂的非线性时间序列数据中的定向光谱信息流. 与金融,气候和神经科学应用的传统模型相比,MF-TFCCA提供了更高的准确性和效率.
科学领域:
- 时间序列分析时间序列分析.
- 信息理论是信息理论.
- 非线性动力学是一种非线性动力学.
背景情况:
- 定向的光谱信息流对于理解金融,气候,地球物理学和神经科学中的复杂系统至关重要.
- 使用矢量自回归 (VAR) 模型的传统光谱格兰杰因果关系 (SGC) 方法与混合频率 (MF) 或非线性时间序列作斗争.
- 现有的参数MF-VAR模型在复杂相互作用下评估SGC时缺乏效率和准确性.
研究的目的:
- 开发一种新的非参数方法,用于量化混合频率和非线性多变量时间序列中的光谱信息流.
- 引入混合频时间频率正规关联分析 (MF-TFCCA) 方法.
- 评估定向信息流的强度和主导频率.
主要方法:
- 提出了一种时间频率正规相关性分析方法 (MF-TFCCA).
- 使用广泛的计算机模拟对MF时间序列和各种相互作用条件验证了该方法.
- 使用替代数据分析评估统计学意义.
- 将MF-TFCCA性能与传统的参数MF-VAR模型进行比较.
主要成果:
- 与MF-VAR模型相比,MF-TFCCA显示出更高的计算效率和检测准确性.
- 该方法成功地确定了光谱信息流的主导驱动频率.
- 在分析来自金融,气候和神经科学的真实世界数据方面,MF-TFCCA被证明是有效的.
结论:
- MF-TFCCA为分析复杂,非线性MF时间序列中的定向光谱信息流提供了一个强大的,计算效率高的,非参数的框架.
- 该方法增强了对跨越各种科学领域的多变量系统的相互依赖性的理解.
- 该方法为需要研究动态相互作用的领域的探索性数据分析提供了有价值的工具.
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