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

Результат исследования: Научные публикации в периодических изданияхстатья по материалам конференциирецензирование

Аннотация

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.

Язык оригиналаанглийский
Номер статьи012025
ЖурналJournal of Physics: Conference Series
Том1715
Номер выпуска1
DOI
СостояниеОпубликовано - 4 янв 2021
СобытиеInternational Conference on Marchuk Scientific Readings 2020, MSR 2020 - Akademgorodok, Novosibirsk, Российская Федерация
Продолжительность: 19 окт 202023 окт 2020

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