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多变量时间序列数据的基于可见度图的细分及其应用
Jun Hu1, Chengbin Chu1, Peican Zhu2
1School of Economics and Management, Fuzhou University, Fuzhou 350108, China.
Chaos (Woodbury, N.Y.)
|September 15, 2023
概括
本研究介绍了一种高效的方法,用于分割多变量时间序列,使用主要组件分析 (PCA),可见度图理论和社区检测. 该方法准确地将复杂的数据分为阶段,以提高性能和降低时间复杂性.
科学领域:
- 数据科学数据科学数据科学
- 时间序列分析时间序列分析
- 网络科学 网络科学
背景情况:
- 多变量时间序列分析由于高维度而存在挑战.
- 现有的细分方法可能会受到维度和计算低效率的诅咒.
研究的目的:
- 开发一种高效准确的方法来分割多变量时间序列.
- 为了克服时间序列数据中维度的诅咒.
- 为合成数据和现实数据提供强大的细分方法.
主要方法:
- 使用主要组件分析 (PCA) 减少尺寸.
- 通过可见度图理论从时间序列数据构建网络.
- 社区检测算法以模块化优化为细分.
主要成果:
- 拟议的方法有效地将多变量时间序列划分为不同的阶段.
- 与最先进的模型相比,在细分方面取得了高准确性.
- 证明了较低的时间复杂性 (O(n^3)) 与优越的细分性能.
结论:
- PCA,可见度图和社区检测的综合方法为多变量时间序列分割提供了高效和准确的解决方案.
- 该方法在生成数据和现实石油期货数据上都被证明是有效的.
- 这种技术解决了维度的诅咒,并提高了细分质量.
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