Revista de Matemática: Teoría y Aplicaciones ISSN Impreso: 1409-2433 ISSN electrónico: 2215-3373

OAI: https://revistas.ucr.ac.cr/index.php/matematica/oai
Information quantifiers and unpredictability in the COVID-19 time-series data
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Keywords

Teoría de la información
Entropía de permutaciones
Complejidad estadística
Metodología de Bandt-Pompe
Transformada Wavelet
Information theory
Permutation entropy
Statistical complexity
Bandt-Pompe methodology
Wavelet transform

How to Cite

Vampa, V., Kowalski, A. M., Losada, M., Portesi, M., & Holik, F. (2023). Information quantifiers and unpredictability in the COVID-19 time-series data. Revista De Matemática: Teoría Y Aplicaciones, 30(1), 1–23. https://doi.org/10.15517/rmta.v30i1.50554

Abstract

We apply different information quantifiers to the study of COVID-19 time series. First, we analyze how the fact of smoothing the curves alters the informational content of the series, by applying the permutation and wavelet entropies to the series of daily new cases using a sliding-window method. In addition, to study how coupled the curves associated with daily new cases of infections and deaths are, we compute the wavelet coherence. Our results show how information quantifiers can be used to analyze the unpredictable behavior of this pandemic in the short and medium terms.

https://doi.org/10.15517/rmta.v30i1.50554
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Copyright (c) 2023 Victoria Vampa, Andrés M. Kowalski, Marcelo Losada, Mariela Portesi, Federico Holik

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