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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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1715; no. 1; pp. 12025 - 12030
Main Authors Efimov, V M, Polunin, D A, Kovaleva, V Y, Efimov, K V
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.01.2021
Subjects
Coronaviruses
COVID-19
Epidemics
Indicators
Physics
Principal components analysis
Time series
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1715/1/012025