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首页> 外文期刊>Industrial and organizational psychology >Determination of time-varying periodicities in unequally spaced time series of OH* temperatures using a moving Lomb-Scargle periodogram and a fast calculation of the false alarm probabilities
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Determination of time-varying periodicities in unequally spaced time series of OH* temperatures using a moving Lomb-Scargle periodogram and a fast calculation of the false alarm probabilities

机译：使用移动的LOMB曲线曲线周期图和快速计算错误警报概率的oh *温度的不平等间隔时间序列中的时变周期的确定

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摘要

We present an approach to analyse time series with unequal spacing. The approach enables the identification of significant periodic fluctuations and the derivation of time-resolved periods and amplitudes of these fluctuations. It is based on the classical Lomb-Scargle periodogram (LSP), a method that can handle unequally spaced time series. Here, we additionally use the idea of a moving window. The significance of the results is analysed with the typically used false alarm probability (FAP). We derived the dependencies of the FAP levels on different parameters that either can be changed manually (length of the analysed time interval, frequency range) or that change naturally (number of data gaps). By means of these dependencies, we found a fast and easy way to calculate FAP levels for different configurations of these parameters without the need for a large number of simulations. The general performance of the approach is tested with different artificially generated time series and the results are very promising. Finally, we present results for nightly mean OH* temperatures that have been observed from Wuppertal (51 degrees N, 7 degrees E; Germany).
机译：我们提出了一种分析时间序列的方法，不平等距离。该方法使得能够识别显着的周期性波动和这些波动的时间分辨期的推导和幅度。它基于典型的LOMB曲线曲线时期（LSP），一种可以处理不平等间隔时间序列的方法。在这里，我们另外使用移动窗口的想法。用典型的误报概率（FAP）分析结果的重要性。我们在不同参数上派生FAP级别的依赖关系，可以手动改变（分析的时间间隔，频率范围的长度）或自然地改变（数据差距）。通过这些依赖项，我们找到了一种快速简便的方法来计算这些参数的不同配置的FAP水平，而无需大量模拟。该方法的一般性能用不同的人工产生的时间序列测试，结果非常有前景。最后，我们展示了从Wuppertal观察到的夜间平均值OH *温度的结果（51℃，7摄氏度;德国）。

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