The PCA-seq method applied to analyze of the dynamics of COVID-19 epidemic indicators
In time series analysis using the SSA method, a univariate series is converted into the multivariate one by shifts. The resulting trajectory matrix is subjected to principal component analysis (PCA). However, the principal components can also be computed using the PCA-Seq method if segments of the o...
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Published in | Journal of physics. Conference series Vol. 1715; no. 1; pp. 12025 - 12030 |
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Format | Journal Article |
Language | English |
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Abstract | In time series analysis using the SSA method, a univariate series is converted into the multivariate one by shifts. The resulting trajectory matrix is subjected to principal component analysis (PCA). However, the principal components can also be computed using the PCA-Seq method if segments of the original series are selected as objects. The matrix of Euclidean distances between the objects can be obtained using any method, which offers additional opportunities for time series analysis compared to the conventional SSA. In this study, the PCA-Seq method was used to analyze the dynamics of COVID-19 epidemic indicators. |
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AbstractList | In time series analysis using the SSA method, a univariate series is converted into the multivariate one by shifts. The resulting trajectory matrix is subjected to principal component analysis (PCA). However, the principal components can also be computed using the PCA-Seq method if segments of the original series are selected as objects. The matrix of Euclidean distances between the objects can be obtained using any method, which offers additional opportunities for time series analysis compared to the conventional SSA. In this study, the PCA-Seq method was used to analyze the dynamics of COVID-19 epidemic indicators. Abstract In time series analysis using the SSA method, a univariate series is converted into the multivariate one by shifts. The resulting trajectory matrix is subjected to principal component analysis (PCA). However, the principal components can also be computed using the PCA-Seq method if segments of the original series are selected as objects. The matrix of Euclidean distances between the objects can be obtained using any method, which offers additional opportunities for time series analysis compared to the conventional SSA. In this study, the PCA-Seq method was used to analyze the dynamics of COVID-19 epidemic indicators. |
Author | Polunin, D A Efimov, K V Kovaleva, V Y Efimov, V M |
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Cites_doi | 10.1101/803684 10.1080/14786440109462720 10.1007/BF02288916 10.1007/BF01896809 10.1093/biomet/53.3-4.325 10.18699/VJ19.584 10.1037/h0071325 |
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Copyright | Published under licence by IOP Publishing Ltd 2021. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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DOI | 10.1088/1742-6596/1715/1/012025 |
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SubjectTerms | Coronaviruses COVID-19 Epidemics Indicators Physics Principal components analysis Time series |
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Title | The PCA-seq method applied to analyze of the dynamics of COVID-19 epidemic indicators |
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