Information quantifiers and wavelet coherence in time-series associated to COVID-19

Victoria Vampa, Andres M. Kowalski, Federico Holik, Marcelo Losada and Mariela Portesi

SCT Proceedings in Interdisciplinary Insights and Innovations, 2024, vol. 2, 10.56294/piii2024303

Abstract: In the present investigation diverse information quantifiers have been applied to the study of time-series of COVID-19. First, it has been analyzed how the smoothing of the curves affects the informative content of the series, using permutation and wavelet entropies for the series of new daily cases, by means of a sliding-windows’ method. Besides, in order to evaluate the relationship between the curves of new daily cases of infections and deaths, the wavelet coherence has been calculated. The results show the utility of information quantifiers to understand the unpredictable behaviour of the pandemics in the short and mean time

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:procee:v:2:y:2024:i::p:1056294piii2024303:id:1056294piii2024303

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