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

Autor:	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.
Předmět:	TIME series analysis FOURIER analysis RESEARCH methodology WAVELETS (Mathematics) SEPARATION of variables DIGITAL signal processing
Zdroj:	Computational Mathematics & Mathematical Physics; Jul2021, Vol. 61 Issue 7, p1106-1117, 12p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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