Information quantifiers and unpredictability in the COVID-19 time-series data
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| Autores: | , , , , |
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| Formato: | artículo original |
| Estado: | Versión publicada |
| Fecha de Publicación: | 2023 |
| Descripción: | 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. |
| País: | Portal de Revistas UCR |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| Lenguaje: | Inglés |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/50554 |
| Acceso en línea: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/50554 |
| Palabra clave: | 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 |