Статья

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

V. Efimov, D. Polunin, V. Kovaleva, K. Efimov,
2021

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. © 2021 Institute of Physics Publishing. All rights reserved.

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Версии

  • 1. Version of Record от 2021-04-27

Метаданные

Об авторах
  • V. Efimov
    Institute of Cytology and Genetics SB RAS, 10 Ac. Lavrentieva Ave., Novosibirsk, 630090, Russian Federation
  • D. Polunin
    Novosibirsk State National Research University, 1 Pirogova St., Novosibirsk, 630090, Russian Federation
  • V. Kovaleva
    Institute of Systematics and Ecology SB RAS, 11 Frunze St., Novosibirsk, 630091, Russian Federation
  • K. Efimov
    Higher School of Economics, National Research University, 20 Myasnitskaya St., Moscow, 101000, Russian Federation
Название журнала
  • Journal of Physics: Conference Series
Том
  • 1715
Выпуск
  • 1
Страницы
  • -
Ключевые слова
  • Harmonic analysis; Euclidean distance; Original series; Principal Components; Trajectory matrix; Univariate; Time series analysis
Издатель
  • IOP Publishing Ltd
Тип документа
  • Conference Paper
Тип лицензии Creative Commons
  • CC
Правовой статус документа
  • Свободная лицензия
Источник
  • scopus