在不同的时间尺度上识别次序相似性
Luciano Zunino1,2, Xavier Porte3, Miguel C Soriano4
1Centro de Investigaciones Ópticas (CONICET La Plata-CIC-UNLP), Gonnet 1897, La Plata, Argentina.
Entropy (Basel, Switzerland)
|January 8, 2025
概括
这项研究引入了一种新指标,以在不同尺度的时间序列数据中找到相似之处. 换詹森-香农距离方法有效地识别复杂系统中的顺序模式.
科学领域:
- 数据科学数据科学数据科学
- 复杂系统分析 复杂系统分析
- 时间序列分析时间序列分析
背景情况:
- 在许多科学领域,时间序列数据分析至关重要.
- 在多个时间尺度上识别相似之处仍然是一个挑战.
- 现有的方法可能无法有效捕捉顺序模式.
研究的目的:
- 为了实现和验证时间序列分析的 permutation Jensen-Shannon 距离.
- 评估该方法在识别时间尺度上的顺序模式和相似性的能力.
- 为了证明这个度量在复杂的光子系统中的实际应用.
主要方法:
- 数字分析以验证多尺度能力.
- 对时间序列数据应用Jensen-Shannon距离 permutation.
- 以不同的时间分辨率对时间序列模式进行比较分析.
主要成果:
- 换詹森-香农距离有效地识别时间序列之间的顺序相似性.
- 该方法的多尺度功能得到了数值验证.
- 在复杂的光子系统中证明了成功的应用,突出了实际的实用性.
结论:
- 变量詹森-香农距离是分析时间序列数据的强大工具.
- 这个度量精确地识别了时间尺度的顺序相似性.
- 该方法在时间序列分析的各种科学学科中具有广泛的适用性.
关键词:
詹森香农的分歧.混乱的半导体激光器半导体激光器延迟的光学反时间多个规模的分析分析.顺序的模式 顺序的模式顺序相似性是指顺序相似性.变换 詹森香农距离 距离变的变是变的变.象征性分析是一种象征性分析.时间序列时间序列更多相关视频
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