摘要
可视图(Visibility Graph,VG)算法为研究时间序列的动力学特性提供了复杂网络的思想.网络的度分布反映了时间序列的动力学特征.通过自回归随机过程和分数布朗运动两种不同数据,分别构建可视图.对比结果表明,在自回归随机过程中,度分布可以用指数函数刻画;而在分数布朗运动中,度分布用幂律函数刻画更为合适.这一结论不但适用于VG算法,同时也适用于水平可视图(Horizontal Visibility Graph,HVG)算法.
Visibility graph has provided much insight to study the dynamics of time series from the perspective complex network. We construct visibility graphs for time series from both auto-regressive stochastic and fractional Brownian motions. Our results suggest that degree distributions of the resulted complex networks of auto-regressive processes are characterized by exponential forms, while that of fractional Brownian motions obey power-law forms. Our conclusions hold for both the traditional visibility graph and its variant horizontal visibility graph.
作者
张蓉
邹勇
ZHANG Rong ZOU Yong(School of Physics and Materials Science, East China Normal University, Shanghai 200241, China)
出处
《华东师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2017年第2期75-80,共6页
Journal of East China Normal University(Natural Science)
基金
国家自然科学基金(11305062)
关键词
可视图
自回归随机过程
分数布朗运动
拟合优度
visibility graph
autoregressive stochastic process
fractional Brownian motion
goodness of fit
