基于网络的复杂多步预测模型,用于超混沌时间序列
Reshmi L B1, Drisya Alex Thumba1, K Asokan2
1Department of Futures Studies, <a href="https://ror.org/05tqa9940">University of Kerala</a>, Kariavattom, Kerala 695 581, India.
Physical review. E
|November 20, 2024
概括
本研究引入了一个复杂的网络模型,用于预测超混乱的时间序列. 这种新的方法提高了预测准确度,并扩大了混乱系统的预测视野.
科学领域:
- 复杂系统科学 复杂系统科学
- 非线性动力学是一种非线性动力学.
- 时间序列分析时间序列分析
背景情况:
- 超混沌的时间序列表现出复杂的,不可预测的动态.
- 传统的预测方法在混乱系统的长期预测准确性方面扎.
- 基于网络的方法为捕捉非线性动态提供了潜力.
研究的目的:
- 为超混沌时间序列开发一种新的复杂的基于网络的预测模型.
- 与现有方法相比,提高准确性和扩大预测范围.
- 展示一种在吸引器内创建离散模型流的程序.
主要方法:
- 从时间序列数据构建一个复杂的网络,作为吸引力的粗粒度表示.
- 将局部振荡模式转换为符号序列,以形成网络节点和边缘.
- 利用网络社区来捕捉和预测动态系统中的模式转换.
主要成果:
- 复杂的网络模型在预测准确性方面明显优于线性第一阶段和其他基于网络的方法.
- 拟议的方法在延长的预测时间范围内实现了优越的预测性能.
- 在高维超混沌系统上证明有效.
结论:
- 复杂网络方法为预测超混沌时间序列提供了一个强大的方法.
- 这种方法通过捕捉局部非线性来提高混乱系统的可预测性.
- 该研究概述了从吸引力动态中开发离散模型的程序.
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