Abstract

Based on the morphological analysis techniques developed under the guidance of Yu.P. Pyt’ev, a method for filtering time series is proposed that is capable of detecting an almost cyclic component with a varying cycle length and varying series members within cycles. The effectiveness of the approach is illustrated as applied to decomposition of time series of atmospheric СО₂ concentrations. After filtering out the series component responsible for diurnal variability, the series residual becomes stationary, so mathematical statistical methods and Fourier analysis can be used for its further investigation. The results are verified by comparing them with Fourier analysis data. A cyclicity with a period longer than one day is studied using Fourier expansion and wavelet analysis of the original series.

中文翻译：

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应用于СО2浓度序列的几乎循环时间序列的形态学和其他研究技术

摘要

基于在 Yu.P. 指导下开发的形态分析技术。Pyt'ev，一种过滤时间序列的方法被提出，它能够检测具有变化循环长度和循环内不同序列成员的几乎循环分量。说明该方法的有效性适用于分解大气 СО ₂浓度的时间序列。滤除引起日变化的级数分量后，级数残差趋于平稳，因此可采用数理统计方法和傅里叶分析对其进行进一步研究。通过将它们与傅立叶分析数据进行比较来验证结果。使用傅立叶展开和原始序列的小波分析来研究周期长于一天的循环。