从顺序模式的组合性质的角度来看,用于时间序列分析的新网络表示
Yun Lu1, Longxin Yao2, Heng Li2
1School of Computer Science and Engineering, Huizhou University, Huizhou, Guangdong 516007, China.
Heliyon
|November 30, 2023
概括
本研究引入了一种用于时间序列分析的新网络方法,使用序列模式和反转数. 这种方法增强了系统行为分析和从脑电图 (EEG) 数据中识别大脑状态.
科学领域:
- 复杂系统分析 复杂系统分析
- 网络科学 网络科学
- 时间序列分析时间序列分析
背景情况:
- 从时间序列数据了解系统行为在许多科学领域都至关重要.
- 现有的顺序网络方法基于顺序模式的过渡概率来分析时间序列.
- 需要一种新的方法来改善时间序列的表示和分析.
研究的目的:
- 提出一种用于时间序列分析的新型网络构建方法.
- 为了利用顺序模式和反转数的组合性质.
- 增强复杂时间序列数据的表示和分析能力.
主要方法:
- 开发了一种基于顺序分区和时间序列的反转数的网络构建方法.
- 定义网络节点作为顺序分区和边缘权重,使用基于反转数的新近距离度量.
- 将该方法应用于随机信号,混乱信号和定量脑电图 (EEG) 数据.
主要成果:
- 证明了该方法对随机和混乱信号的网络表示的潜力.
- 通过使用定量EEG成功应用了该方法来识别使用定量EEG的三个不同的大脑状态.
- 与基于AUC值的现有顺序网络方法相比,拟议的方法显示出稍微更强的区分能力.
结论:
- 拟议的网络构建方法为时间序列表示提供了一个新的途径.
- 这种方法可以有效量化时间序列,用于特征提取和模式学习.
- 这种方法在推进包括神经科学在内的各种科学领域的时间序列分析方面表现有前途.
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