查普曼-科尔莫戈罗夫测试用于估计两个合过程的内存长度
H Motahari1, M Ghanbarzadeh Noudehi2, T Jamali1
1Department of Physics, Shahid Beheshti University, Evin, Tehran, 1983969411, Iran.
Scientific reports
|April 4, 2025
概括
这项研究引入了一个通用的查普曼-科尔摩戈罗夫方程 (CKE) 来准确地测量复杂系统中的长期记忆. 该方法有效地分析了合的时间序列,揭示了加密货币动态中的重大相互依赖.
科学领域:
- 复杂系统分析 复杂系统分析
- 时间序列建模时间序列建模
- 统计力学 统计力学
背景情况:
- 现实世界的过程经常表现出远程记忆,超过了简单的马科维依赖关系.
- 系统状态可以受到多个相互依赖的过程的过去状态的影响.
研究的目的:
- 介绍一个通用的查普曼-科尔摩戈罗夫方程 (CKE) 用于估计复杂的,依赖的过程中的内存大小.
- 为了验证一般化的CKE在测量内存长度方面的准确性.
- 将该方法应用于现实世界合时间序列数据,特别是加密货币.
主要方法:
- 开发一个通用的查普曼-科尔摩戈罗夫方程 (CKE).
- 使用自回归模型模拟记忆的合成合时间序列的生成.
- 应用广义的CKE来分析加密货币市场数据.
主要成果:
- 一般化的CKE在合成数据中测量预定义内存长度时表现出高精度.
- 对加密货币数据的分析揭示了过去对当前状态的重大影响.
- 确定了不同加密货币时间序列之间的实质性相互依赖.
结论:
- 一般化的CKE是一个强大的工具,用于在多个依赖的复杂系统中量化内存.
- 该方法证实了加密货币市场内的相互联系和记忆效应.
- 该方法为预测各种领域的合时间序列数据提供了潜力.
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