论文标题
MAUNA LOA CO2数据的自适应时间序列分析数据：基于TVF-EMD的季节性变异性的逐渐降低和提取
Adaptive time series analysis of Mauna Loa CO2 data: tvf-EMD based detrend and extraction of seasonal variability
论文作者
论文摘要
自适应时间序列分析已应用于研究CO2浓度数据的变异性，每周在Mauna LOA监测站进行采样。由于它可以减轻模式混合的能力，因此使用了最近的时间变化过滤器经验模式分解（TVF-EMD）方法来提取局部窄带振荡模式。为了进行数据分析，我们开发了TVF-EMD算法的Python实现，称为PYTVFEMD。算法允许提取趋势以及六个月和一年的周期性，即使分析的数据很吵，也没有模式混合。此外，减去此类模式得到了残差，这被发现通过正态分布描述。还研究了异常值的发生，发现它们在数据集末端的数量较高，对应于以较小的黑子数为特征的太阳周期。与太阳周期活动有关，在这方面也观察到了残差的更明显的振荡。
Adaptive time series analysis has been applied to investigate variability of CO2 concentration data, sampled weekly at Mauna Loa monitoring station. Due to its ability to mitigate mode mixing, the recent time varying filter Empirical Mode Decomposition (tvf-EMD) methodology is employed to extract local narrowband oscillatory modes. In order to perform data analysis, we developed a Python implementation of the tvf-EMD algorithm, referred to as pytvfemd. The algorithm allowed to extract the trend and both the six month and the one year periodicities, without mode mixing, even though the analysed data are noisy. Furthermore, subtracting such modes the residuals are obtained, which are found to be described by a normal distribution. Outliers occurrence was also investigated and it is found that they occur in higher number toward the end of the dataset, corresponding to solar cycles characterised by smaller sunspot numbers. A more pronounced oscillation of the residuals is also observed in this regard, in relation to the solar cycles activity too.
