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Abstract：
We investigate whether it is possible to distinguish chaotic time series from random time series using network theory. In this perspective, we selected four methods to generate graphs from time series: the natural, the horizontal, the limited penetrable horizontal visibility graph, and the phase space reconstruction method. These methods claim that the distinction of chaos from randomness is possible by studying the degree distribution of the generated graphs. We evaluated these methods by computing the results for chaotic time series from the 2D Torus Automorphisms, the chaotic Lorenz system, and a random sequence derived from the normal distribution. Although the results confirm previous studies, we found that the distinction of chaos from randomness is not generally possible in the context of the above methodologies.
摘要：
我们研究是否有可能使用网络理论来区分混沌时间序列和随机时间序列。从这个角度来看,我们选择了四种方法从时间序列中生成图形：自然，水平的,有限的可穿透水平能见度图，和相空间重构方法。这些方法声称，通过研究生成图的度分布，可以将混沌与随机性区分开。我们通过计算来自2DTorus自同构的混沌时间序列的结果来评估这些方法，混沌的洛伦兹系统,和从正态分布导出的随机序列。尽管结果证实了以前的研究，我们发现，在上述方法的背景下，混沌与随机性的区别通常是不可能的。
