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对反复性量化措施的插值和抽样效应
Nils Antary1,2, Martin H Trauth3, Norbert Marwan1,3
1Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, 14473 Potsdam, Germany.
Chaos (Woodbury, N.Y.)
|October 2, 2023
概括
对用于复发量化分析 (RQA) 的非均采样数据的插入可能会导致结果偏差. 本研究分析了RQA的测量差异,并提出了整数插值比的校正方案,以提高数据分析的准确性.
科学领域:
- 复杂系统分析 复杂系统分析
- 数据科学数据科学数据科学
- 时间序列分析时间序列分析
背景情况:
- 复发图和复发量化分析 (RQA) 对于研究各种科学领域的复杂系统动态,周期性和制度变化至关重要.
- 标准RQA要求统一采样数据,这对医学,古气候学和天体物理学中常见的非统一数据集构成了挑战.
- 数据插入是一个常见的解决方案,但可以在RQA测量中引入偏差,特别是那些对复杂度图中的线结构敏感的数据.
研究的目的:
- 系统地分析不同采样和插值方法对RQA测量,特别是平均对角线长度的影响.
- 调查插值采样率对现实世界非均采样数据的RQA指标的影响.
- 开发和提出一个纠正方案,以减轻RQA中数据插值引入的偏差.
主要方法:
- 利用原型模型系统对RQA进行系统分析,在不同的采样和插值条件下测量差异.
- 应用于对非均采样的真实世界数据集 (例如医疗,古气候,天体物理数据) 的插值技术.
- 评估了通过不同插值策略处理的数据的复杂度图表的平均对角线长度.
主要成果:
- 证明了插值可以在RQA测量中引入显著的偏差,特别是影响平均对角线长度.
- 显示实数据中平均对角线长度的行为高度依赖于在插值过程中选择的采样率.
- 确定,当插值比为整数时,建议的校正方案可以有效地纠正插值诱导的偏差.
结论:
- 对RQA的非均采样数据的插入需要仔细考虑采样率,以避免引入偏差.
- 开发的校正方案提供了一种可行的方法来提高RQA测量的准确性,这些测量是从用整数比率进行插入的数据中获得的.
- 这项工作为将RQA应用于各种科学学科中具有挑战性的,不统一的样本数据集提供了关键的进步.
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