从可见度和相位空间重建图的度分布来区分混沌与随机是不可能的
Alexandros K Angelidis1,2, Konstantinos Goulas1, Charalampos Bratsas2
1Department of Mathematics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Entropy (Basel, Switzerland)
|April 26, 2024
概括
网络理论方法很难区分混乱的时间序列和随机的时间序列. 分析图形度分布并不能可靠地区分混乱和随机数据,这挑战了之前的发现.
科学领域:
- 复杂的系统复杂的系统.
- 网络理论 网络理论
- 时间序列分析时间序列分析
背景情况:
- 在许多科学领域,区分混沌和随机时间序列至关重要.
- 网络理论为分析时间序列数据提供了潜在的工具.
- 以前的研究表明,基于图的方法可以区分混乱和随机性.
研究的目的:
- 研究网络理论方法在区分混乱和随机时间序列方面的有效性.
- 评估用于时间序列分析的特定图形生成技术.
主要方法:
- 使用了四种图形生成方法:自然,水平,有限的可穿透水平可见度图形和相位空间重建.
- 将这些方法应用于混乱的时间序列 (2D Torus Automorphisms,Lorenz系统) 和随机正常分布序列.
- 分析了生成的图形的度分布.
主要成果:
- 证实了关于这些方法的应用的先前发现.
- 发现,度分布分析并没有在混乱和随机时间序列之间进行一致的区分.
- 评估的网络理论方法通常不足以进行这种区别.
结论:
- 基于图形度分布的网络理论方法通常无法区分混沌时间序列和随机时间序列.
- 需要进一步的研究来开发更强大的混沌时间序列分析方法.
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