Department of Statistics, Pennsylvania State University, State College, PA, United States of America.
CGStat LLC, Raleigh, NC, United States of America.
PLoS One. 2018 Apr 23;13(4):e0195360. doi: 10.1371/journal.pone.0195360. eCollection 2018.
In environmental epidemiology, it is often encountered that multiple time series data with a long-term trend, including seasonality, cannot be fully adjusted by the observed covariates. The long-term trend is difficult to separate from abnormal short-term signals of interest. This paper addresses how to estimate the long-term trend in order to recover short-term signals. Our case study demonstrates that the current spline smoothing methods can result in significant positive and negative cross-correlations from the same dataset, depending on how the smoothing parameters are chosen. To circumvent this dilemma, three classes of time series smoothers are proposed to detrend time series data. These smoothers do not require fine tuning of parameters and can be applied to recover short-term signals. The properties of these smoothers are shown with both a case study using a factorial design and a simulation study using datasets generated from the original dataset. General guidelines are provided on how to discover short-term signals from time series with a long-term trend. The benefit of this research is that a problem is identified and characteristics of possible solutions are determined.
在环境流行病学中，经常会遇到多个具有长期趋势的时间序列数据，包括季节性，不能完全通过观察到的协变量进行调整。长期趋势很难与感兴趣的异常短期信号分离。本文讨论了如何估计长期趋势，以便恢复短期信号。我们的案例研究表明，当前的样条平滑方法可以根据平滑参数的选择，从同一数据集产生显著的正相关和负相关。为了避免这种困境，提出了三类时间序列平滑器来去除时间序列数据的趋势。这些平滑器不需要微调参数，并且可以应用于恢复短期信号。通过使用析因设计的案例研究和使用从原始数据集生成的数据集的模拟研究，展示了这些平滑器的性质。提供了有关如何从具有长期趋势的时间序列中发现短期信号的一般指导原则。这项研究的好处在于确定了一个问题，并确定了可能解决方案的特征。
