一种相互信息统计,用于评估动态系统的状态空间分区
Jason Lu1, Michael Small1,2
1Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia.
Chaos (Woodbury, N.Y.)
|November 1, 2024
概括
我们开发了一个相互信息统计,以评估动态系统中的信息. 该方法识别了基于轨迹的最佳分区,通过新的加权顺序分区增强了时间序列分析.
科学领域:
- 动态系统和信息理论.
- 时间序列分析和数据挖掘
背景情况:
- 动态系统产生复杂的轨迹,需要有效的分区进行分析.
- 通过状态空间分区编码的量化信息对于理解系统动态至关重要.
- 像顺序分区这样的现有方法提供了洞察力,但可以进一步优化.
研究的目的:
- 引入一种新的相互信息统计,用于量化动态系统分区中的信息.
- 评估基于轨迹历史的分区的性能.
- 为了开发和验证一种改进的时间序列分析方法,加权序列分区.
主要方法:
- 在符号轨迹历史和分区分配之间计算相互信息.
- 在不同的分区策略中比较信息理论性能.
- 引入和优化加权顺序分区使用拟议的统计.
主要成果:
- 拟议的相互信息统计表现出可预测和一致的行为.
- 基于轨迹历史的分区,特别是顺序分区,表现出卓越的性能.
- 在时间序列分析中,加权序列分区显示了与标准序列分区相比的显著改进.
结论:
- 相互信息统计有效量化了动态系统分区中的信息.
- 权重顺序分区代表了时间序列分析的重大进步.
- 该方法适用于现实世界的实验数据集,证明了其实际实用性.
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