Morphological and Other Research Techniques for Almost Cyclic Time Series as Applied to СО2 Concentration Series

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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computational mathematics and mathematical physics 2021-07, Vol.61 (7), p.1106-1117
Hauptverfasser: Avilov, V. K., Aleshnovskii, V. S., Bezrukova, A. V., Gazaryan, V. A., Zyuzina, N. A., Kurbatova, Yu. A., Tarbaev, D. A., Chulichkov, A. I., Shapkina, N. E.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 СО 2 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.
ISSN:0965-5425
1555-6662
DOI:10.1134/S0965542521070046