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摘要翻译：
具有不同动力学性质的时间序列的超族现象可以用时间序列在相空间中的最近邻网络中观察到的模体秩模式来表征。然而，超科分类的决定因素尚不清楚。我们利用分数布朗运动(FBMs)和多重分形随机游动(MRWs)研究线性时间相关性和多重分形性的影响来解决这个问题。数值研究表明，超家族现象的分类是由时间序列的DFA标度指数α唯一决定的。在模拟数据中只观察到四个模体模式，它们由三个DFA标度指数$\Alpha\Simeq0.25$，$\Alpha\Simeq0.35$，$\Alpha\Simeq0.45$.通过股市指数和湍流速度信号验证了计算结果的有效性。
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英文标题：
《Superfamily classification of nonstationary time series based on DFA
scaling exponents》
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作者：
Chuang Liu and Wei-Xing Zhou (ECUST)
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最新提交年份：
2009
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分类信息：
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：
The superfamily phenomenon of time series with different dynamics can be characterized by the motif rank patterns observed in the nearest-neighbor networks of the time series in phase space. However, the determinants of superfamily classification are unclear. We attack this problem by studying the influence of linear temporal correlations and multifractality using fractional Brownian motions (FBMs) and multifractal random walks (MRWs). Numerical investigations unveil that the classification of superfamily phenomenon is uniquely determined by the detrended fluctuation analysis (DFA) scaling exponent $\alpha$ of the time series. Only four motif patterns are observed in the simulated data, which are delimited by three DFA scaling exponents $\alpha \simeq 0.25$, $\alpha \simeq 0.35$ and $\alpha \simeq 0.45$. The validity of the result is confirmed by stock market indexes and turbulence velocity signals.
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PDF链接：
https://arxiv.org/pdf/0912.2016
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