马超统计学和复杂时间序列分析
1Universidad de La Serena, Instituto de Investigación Multidisciplinario en Ciencia y Tecnología, La Serena 170000, Chile and Departamento de Física, Universidad de La Serena, Avenue Juan Cisternas 1200, La Serena 170000, Chile.
Physical review. E
|August 19, 2025
概括
这项研究分析了超统计时间序列的复杂性,使用多分形延迟波动分析 (MFDFA). 结果显示,复杂性取决于形状参数 ν 和状态密度参数 μ,它们补充地描述了系统行为.
科学领域:
- 复杂系统分析 复杂系统分析
- 统计物理 统计物理
- 时间序列分析时间序列分析
背景情况:
- 超级统计 (SS) 提供了一个框架来建模具有波动参数的复杂系统.
- 了解这些系统的复杂性对于准确的建模和预测至关重要.
研究的目的:
- 调查超统计时间序列的复杂性,这些时间序列为波动参数生成了马分布.
- 为了确定超统计参数对系统复杂性的影响.
- 用这些参数验证回归模型来描述使用这些参数的复杂性.
主要方法:
- 产生具有不同参数的合成超统计时间序列.
- 应用多分形延迟波动分析 (MFDFA) 来通过奇点光谱宽度 (Δα) 来量化复杂性.
- 使用多重回归模型来评估超统计参数和复杂性之间的关系.
主要成果:
- 系统复杂性 (Δα) 很大程度上取决于马分布的形状参数 (ν).
- 状态密度参数 (μ) 也增加了复杂性.
- 参数 μ 和 ν 在描述系统复杂性时互补作用,没有显著的相互作用.
结论:
- 开发的回归模型有效地描述了模拟超统计时间序列的复杂性.
- 这些发现得到了SYM-H大小数据集的分析的支持,这表明它适用于现实世界的复杂系统,如太空天气.
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