From the time series to the complex networks: The parametric natural visibility graph
From the time series to the complex networks: The parametric natural visibility graph
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DOI：
10.1016/j.physa.2014.07.002
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发表时间：
2014-11-15
影响因子：
3.3
通讯作者：
Snarskii, A. A.
中科院分区：
文献类型：
--
作者：
Bezsudnov, I. V.;Snarskii, A. A.
提出了将时间序列映射到复杂网络(图)的自然可见图(NVG)算法的改进。提出了参数自然可见图(PNVG)算法。PNVG由NVG链接组成，这些链接满足由新引入的连续参数视角确定的附加约束。视角的改变改变了PNVG及其属性，如图的平均结点度、平均链长以及构建图的聚类数等。我们计算和分析了健康和疾病患者的随机(不相关、相关和分形)和心律时间序列等不同类型的时间序列随视角的不同PNVG属性。对PNVG的不同性质的研究表明，视角为刻画传统算法中不可见的时间序列的结构提供了一种新的方法。PNVG方法使我们能够区分、识别和详细描述各种时间序列。(C)2014爱思唯尔B.V.保留所有权利。
We present the modification of natural visibility graph (NVG) algorithm used for the mapping of the time series to the complex networks (graphs). We propose the parametric natural visibility graph (PNVG) algorithm. The PNVG consists of NVG links, which satisfy an additional constraint determined by a newly introduced continuous parameter the view angle. The alteration of view angle modifies the PNVG and its properties such as the average node degree, average link length of the graph as well as cluster quantity of built graph, etc. We calculated and analyzed different PNVG properties depending on the view angle for different types of the time series such as the random (uncorrelated, correlated and fractal) and cardiac rhythm time series for healthy and ill patients. Investigation of different PNVG properties shows that the view angle gives a new approach to characterize the structure of the time series that are invisible in the conventional version of the algorithm. It is also shown that the PNVG approach allows us to distinguish, identify and describe in detail various time series. (C) 2014 Elsevier B.V. All rights reserved.