机器学习的时间序列数据使用持久的同质性
1Gifu University School of Medicine, Yanagido 1-1, Gifu, 501-1194, Japan. ichinomiya.takashi.f5@f.gifu-u.ac.jp.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种新的时间序列分析方法,使用复杂度图和持久同质性. 该方法有效地提取了用于识别系统转换和分类生物信号的关键特征.
科学领域:
- 复杂系统分析 复杂系统分析
- 拓数据分析 拓数据分析
- 生物医学信号处理
背景情况:
- 时间序列分析的传统持久同质学面临着高的计算成本.
- 回复图提供了一种计算效率高的方法来可视化动态系统.
研究的目的:
- 开发一种新的,计算效率高的时间序列分析方法.
- 从时间序列数据中提取有意义的拓特征.
- 证明该方法在识别系统动态和分类生物信号方面的有效性.
主要方法:
- 从时间序列数据集生成复制图.
- 使用持久的同类学提取拓特征.
- 使用持久图像向量化拓数据,并通过非负矩阵因子化减少维度.
主要成果:
- 在楚亚的系统中,成功地确定了周期转变为混乱和混乱转变为混乱的过渡.
- 使用电肌图数据区分健康,神经病和肌病患者.
- 根据提取的特征,实现了基于心电图数据的准确分类.
结论:
- 拟议的方法有效地从时间序列数据中捕获基本信息.
- 这种方法为分析复杂的动态系统和生物医学信号提供了强大的工具.
- 提取的拓特征具有特色,并且对各种分类任务有用.
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