时间序列中的超统计波动：股价动态和turbulence的应用
Superstatistical fluctuations in time series: Applications to share-price dynamics and turbulence
摘要：
我们报告了一个一般的技术来研究一个给定的实验时间序列与超统计量。超统计量概念的适用性的关键是存在一个参数$β$，与所考虑的复杂系统的其他时间尺度相比，该参数在大的时间尺度上波动。所提出的方法提取出一个给定的数据集的主要超统计参数，并检查超统计模型假设的有效性。我们彻底测试的方法与替代数据集。然后，超统计方法的适用性说明使用真实的实验数据。我们研究了两个例子，在湍流泰勒-库埃特流的速度时间序列和时间序列的一些股票市场指数的收盘价的日志回报。
英文摘要：
We report a general technique to study a given experimental time series with superstatistics. Crucial for the applicability of the superstatistics concept is the existence of a parameter $β$ that fluctuates on a large time scale as compared to the other time scales of the complex system under consideration. The proposed method extracts the main superstatistical parameters out of a given data set and examines the validity of the superstatistical model assumptions. We test the method thoroughly with surrogate data sets. Then the applicability of the superstatistical approach is illustrated using real experimental data. We study two examples, velocity time series measured in turbulent Taylor-Couette flows and time series of log returns of the closing prices of some stock market indices.
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