摘要翻译：
我们提出了一种正确分析随机时间序列的新方法，将时间序列波动的动力学映射到一个合适的非平衡表面增长问题上。在该框架中，波动采样时间间隔扮演时间变量的角色，而物理时间被视为空间变量的模拟。通过这种方法，我们发现许多现实世界时间序列的涨落都满足Family-Viscek动态标度ANSATZ的模拟。这一发现允许使用动力学粗糙化理论的有力工具对现实世界时间序列的波动进行分类、建模和预测。
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英文标题：
《Dynamic scaling approach to study time series fluctuations》
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作者：
Alexander S. Balankin
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最新提交年份：
2008
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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：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：
  We propose a new approach for properly analyzing stochastic time series by mapping the dynamics of time series fluctuations onto a suitable nonequilibrium surface-growth problem. In this framework, the fluctuation sampling time interval plays the role of time variable, whereas the physical time is treated as the analog of spatial variable. In this way we found that the fluctuations of many real-world time series satisfy the analog of the Family-Viscek dynamic scaling ansatz. This finding permits to use the powerful tools of kinetic roughening theory to classify, model, and forecast the fluctuations of real-world time series.
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PDF链接：
https://arxiv.org/pdf/0808.3269