摘要翻译：
近年来，可见性图被引入到时间序列分析中，它将时间序列映射到一个复杂的网络中。本文提出了一种新的可见性算法“交叉可见性”，它揭示了两个耦合时间序列的共轭性。这两个时间序列之间的对应关系被映射到一个网络，“交叉可见图”，以证明它们之间的相关性。我们将该算法应用于几个相关和不相关的时间序列，由线性平稳ARFIMA过程生成。结果表明，与具有幂律自相关的相关时间序列相关的互可见性图是无标度的。当时间序列不相关时，其互视网络的度分布偏离幂律。为了更好地阐明这一过程，我们将该算法应用于两家公司的真实金融交易数据，并观察到它们在动力学上存在显著的小尺度耦合。
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英文标题：
《Coupling between time series: a network view》
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作者：
Saeed Mehraban, Amirhossein Shirazi, Maryam Zamani, Gholamreza Jafari
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最新提交年份：
2013
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分类信息：

一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
  Recently, the visibility graph has been introduced as a novel view for analyzing time series, which maps it to a complex network. In this paper, we introduce new algorithm of visibility, "cross-visibility", which reveals the conjugation of two coupled time series. The correspondence between the two time series is mapped to a network, "the cross-visibility graph", to demonstrate the correlation between them. We applied the algorithm to several correlated and uncorrelated time series, generated by the linear stationary ARFIMA process. The results demonstrate that the cross-visibility graph associated with correlated time series with power-law auto-correlation is scale-free. If the time series are uncorrelated, the degree distribution of their cross-visibility network deviates from power-law. For more clarifying the process, we applied the algorithm to real-world data from the financial trades of two companies, and observed significant small-scale coupling in their dynamics.
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PDF链接：
https://arxiv.org/pdf/1301.1010