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...
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| Veröffentlicht in: | Computational mathematics and mathematical physics 2021-07, Vol.61 (7), p.1106-1117 |
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| container_title | Computational mathematics and mathematical physics |
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| creator | 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. |
| description | 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. |
| doi_str_mv | 10.1134/S0965542521070046 |
| format | Article |
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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.</description><identifier>ISSN: 0965-5425</identifier><identifier>EISSN: 1555-6662</identifier><identifier>DOI: 10.1134/S0965542521070046</identifier><language>eng</language><publisher>Moscow: Pleiades Publishing</publisher><subject>Computational Mathematics and Numerical Analysis ; Filtration ; Fourier analysis ; Information Science ; Mathematics ; Mathematics and Statistics ; Morphology ; Statistical methods ; Time series ; Wavelet analysis</subject><ispartof>Computational mathematics and mathematical physics, 2021-07, Vol.61 (7), p.1106-1117</ispartof><rights>Pleiades Publishing, Ltd. 2021. ISSN 0965-5425, Computational Mathematics and Mathematical Physics, 2021, Vol. 61, No. 7, pp. 1106–1117. © Pleiades Publishing, Ltd., 2021. Russian Text © The Author(s), 2021, published in Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki, 2021, Vol. 61, No. 7, pp. 1113–1124.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2316-635cdc351113b556161b61612fc8023f175ca5750e21aed70ff54fac1bcb09283</citedby><cites>FETCH-LOGICAL-c2316-635cdc351113b556161b61612fc8023f175ca5750e21aed70ff54fac1bcb09283</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1134/S0965542521070046$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1134/S0965542521070046$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Avilov, V. K.</creatorcontrib><creatorcontrib>Aleshnovskii, V. S.</creatorcontrib><creatorcontrib>Bezrukova, A. V.</creatorcontrib><creatorcontrib>Gazaryan, V. A.</creatorcontrib><creatorcontrib>Zyuzina, N. A.</creatorcontrib><creatorcontrib>Kurbatova, Yu. A.</creatorcontrib><creatorcontrib>Tarbaev, D. A.</creatorcontrib><creatorcontrib>Chulichkov, A. I.</creatorcontrib><creatorcontrib>Shapkina, N. E.</creatorcontrib><title>Morphological and Other Research Techniques for Almost Cyclic Time Series as Applied to СО2 Concentration Series</title><title>Computational mathematics and mathematical physics</title><addtitle>Comput. Math. and Math. Phys</addtitle><description>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. 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| source | SpringerNature Journals |
| subjects | Computational Mathematics and Numerical Analysis Filtration Fourier analysis Information Science Mathematics Mathematics and Statistics Morphology Statistical methods Time series Wavelet analysis |
| title | Morphological and Other Research Techniques for Almost Cyclic Time Series as Applied to СО2 Concentration Series |
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