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

V. M. Efimov, D. A. Polunin, V. Y. Kovaleva, K. V. Efimov

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish
Article number012025
JournalJournal of Physics: Conference Series
Volume1715
Issue number1
DOIs
Publication statusPublished - 4 Jan 2021
EventInternational Conference on Marchuk Scientific Readings 2020, MSR 2020 - Akademgorodok, Novosibirsk, Russian Federation
Duration: 19 Oct 202023 Oct 2020

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