Standard

The PCA-seq method applied to analyze of the dynamics of COVID-19 epidemic indicators. / Efimov, V. M.; Polunin, D. A.; Kovaleva, V. Y. et al.

In: Journal of Physics: Conference Series, Vol. 1715, No. 1, 012025, 04.01.2021.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Efimov, VM, Polunin, DA, Kovaleva, VY & Efimov, KV 2021, 'The PCA-seq method applied to analyze of the dynamics of COVID-19 epidemic indicators', Journal of Physics: Conference Series, vol. 1715, no. 1, 012025. https://doi.org/10.1088/1742-6596/1715/1/012025

APA

Efimov, V. M., Polunin, D. A., Kovaleva, V. Y., & Efimov, K. V. (2021). The PCA-seq method applied to analyze of the dynamics of COVID-19 epidemic indicators. Journal of Physics: Conference Series, 1715(1), [012025]. https://doi.org/10.1088/1742-6596/1715/1/012025

Vancouver

Efimov VM, Polunin DA, Kovaleva VY, Efimov KV. The PCA-seq method applied to analyze of the dynamics of COVID-19 epidemic indicators. Journal of Physics: Conference Series. 2021 Jan 4;1715(1):012025. doi: 10.1088/1742-6596/1715/1/012025

Author

Efimov, V. M. ; Polunin, D. A. ; Kovaleva, V. Y. et al. / The PCA-seq method applied to analyze of the dynamics of COVID-19 epidemic indicators. In: Journal of Physics: Conference Series. 2021 ; Vol. 1715, No. 1.

BibTeX

@article{ef1a11b7442e4d7e90cda002ef416ec2,
title = "The PCA-seq method applied to analyze of 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 = "Efimov, {V. M.} and Polunin, {D. A.} and Kovaleva, {V. Y.} and Efimov, {K. V.}",
note = "Funding Information: This work was supported by the Russian Foundation for Basic Research ?project no. 19 07 00658 a) and the Budget Project of the Institute of Cytology and Genetics, SB RAS ?project no. 0324 2019 0040 C 01). Publisher Copyright: {\textcopyright} 2021 Institute of Physics Publishing. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; International Conference on Marchuk Scientific Readings 2020, MSR 2020 ; Conference date: 19-10-2020 Through 23-10-2020",
year = "2021",
month = jan,
day = "4",
doi = "10.1088/1742-6596/1715/1/012025",
language = "English",
volume = "1715",
journal = "Journal of Physics: Conference Series",
issn = "1742-6588",
publisher = "IOP Publishing Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - The PCA-seq method applied to analyze of the dynamics of COVID-19 epidemic indicators

AU - Efimov, V. M.

AU - Polunin, D. A.

AU - Kovaleva, V. Y.

AU - Efimov, K. V.

N1 - Funding Information: This work was supported by the Russian Foundation for Basic Research ?project no. 19 07 00658 a) and the Budget Project of the Institute of Cytology and Genetics, SB RAS ?project no. 0324 2019 0040 C 01). Publisher Copyright: © 2021 Institute of Physics Publishing. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/1/4

Y1 - 2021/1/4

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85100749912&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/1715/1/012025

DO - 10.1088/1742-6596/1715/1/012025

M3 - Conference article

AN - SCOPUS:85100749912

VL - 1715

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012025

T2 - International Conference on Marchuk Scientific Readings 2020, MSR 2020

Y2 - 19 October 2020 through 23 October 2020

ER -

ID: 27879500