Palabras clave
Entropía de permutaciones
Complejidad estadística
Metodología de Bandt-Pompe
Transformada Wavelet
Information theory
Permutation entropy
Statistical complexity
Bandt-Pompe methodology
Wavelet transform
Cómo citar
Resumen
Aplicamos diferentes cuantificadores de informacion al estudio de series temporales de COVID-19. En primer lugar, analizamos como el hecho de suavizar las curvas altera el
contenido de informacion de la serie, aplicando la entropia de permutaciones y la entropia wavelet a la serie de casos diarios nuevos mediante un metodo de ventana movil. Ademas, para estudiar que tan acopladas estan las curvas asociadas con los nuevos casos diarios de infecciones y muertes, calculamos la coherencia wavelet. Nuestros resultados muestran como se pueden utilizar cuantificadores de información para analizar el comportamiento impredecible de esta pandemia en el corto y mediano plazo.
Citas
M. B. Arouxet, A. F. Bariviera, V. E. Pastor, V. Vampa, COVID-19 impact on cryptocurrencies: evidence from a wavelet-based Hurst exponent, Physica A 596(2022), 127170. Doi: 10.1016/j.physa.2022.127170
C. Bandt, B. Pompe, Permutation entropy: a natural complexity measure for time series, Phys. Rev. Lett. 88(2002), 174102. Doi: 10.1103/PhysRevLett.88.174102
S. Blanco, A. Figliola, R. Quian Quiroga, O.A. Rosso, E. Serrano, Time-frequency analysis of electroencephalogram series. III. Wavelet packets and information cost function. Phys. Rev. E 57(1998), 932. Doi: 10.1103/PhysRevE.57.932
C. Chum, An Introduction to Wavelets, Academic Press, New York, 1992. Available from: https://www.elsevier.com/books/an-introduction-to-wavelets/chui/978-0-12-174592-9.
J. Contreras-Reyes, Fisher information and uncertainty principle for skewgaussian random variables, Fluctuation and Noise Letters 20(2021) no.5, 21500395. Doi: 10.1142/S0219477521500395
I. Daubechies, Ten Lectures on Wavelets, SIAM, 61, 1992. Doi:
1137/1.9781611970104.fm
L. Fernandes, F. Araujo, M. Silva, B. Acioli-Santos, Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers, Results in Physics 26(2021) 104306. Doi: 10.1016/j.rinp.2021.104306
L. Fernandes, F. De Araujo, J. Silva, M. Silva, Insights into the predictability and similarity of COVID-19 worldwide lethality, Fractals 29(2021), no. 07, 2150221. Doi: 10.1142/S0218348X21502212
L. Gamero, A. L. Plastino, M. E. Torres, Wavelet analysis and nonlinear dynamics in a nonextensive setting, Physica A 246(1997), 487-509. Doi: 10.1016/S0378-4371(97)00367-1
M. Henry, G. Judge, Permutation entropy & information recovery in nonlinear dynamic economic time series, Econometrics 7(2019) no. 1, 10. Doi: 10.3390/econometrics7010010
A. M. Kowalski, M. Portesi, V. Vampa, M. Losada, F. Holik, Entropy-based informational study of the COVID-19 series of data, Mathematics 10(2022), no. 23, 4590. Doi: 10.3390/math10234590
A. M. Kowalski, R. Rossignoli, E.M.F. Curado, Eds. Concepts and Recent Advances in Generalized Information Measures and Statistics, Bentham SciencePublishers 2013. Doi: 10.2174/97816080576031130101
M. Kumar, R. Pachori, U. Acharya, Automated diagnosis of myocardial infarction ECG signals using sample entropy in flexible analytic wavelet transform framework, Entropy 19(2017), no. 9, 488. Doi: 10.3390/e19090488
X. Li, G. Ouyang, D. Richards, Predictability analysis of absence seizures with permutation entropy, Epilepsy Research 77(2007), 70-74. Doi: 10.1016/j.eplepsyres.2007.08.002
D. Meintrup, M. Nowak-Machen, S. Borgmann, Nine Months of COVID-19 Pandemic in Europe: A Comparative Time Series Analysis of Cases and Fatalities in 35 Countries, International Journal of Environmental Research and Public Health 18(2021), no. 12, 6680.
Doi: 10.3390/ijerph18126680
F. Mitroi-Symeonidis, I. Anghel, O. Lalu, C. Popa, The Permutation Entropy and its Applications on Fire Tests Data, J. Appl. Comput. Mech. 6(2020), no. SI, 1380-1393.
Doi: 10.22055/jacm.2020.34707.2464f
F. Mitroi-Symeonidis, I. Anghel, A. Tozzi, Preventing a COVID-19 pandemic flashover (electronic response to: Day M. 2020. Covid-19: identifying and isolating asymptomatic people helped eliminate virus in Italian village), 2020. Doi: 10.13140/RG.2.2.34525.97768
O. Nicolis, J. Mateu, J. Contreras-Reyes, Wavelet-Based Entropy Measures to Characterize Two-Dimensional Fractional Brownian Fields, Entropy 22(2020), no. 2, 196. Doi: 10.3390/e22020196
G. Ouyang, Permutation entropy, 2021. Available at: https://www.mathworks.com/matlabcentral/fileexchange/37289-permu
tation-entropy. Retrieved June 23, 2021.
F. Olivares, A. L. Plastino, O. A. Rosso, Ambiguities in Bandt-Pompe’s methodology for local entropic quantifiers, Physica A 391(2012), 2518-2526. Doi: 10.1016/j.physa.2011.12.033
G. Ouyang, J. Li, X. Liu, X. Li, Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis, Epilepsy Research 104(2013), no. 3, 246-252.
Doi: j.eplepsyres.2012.11.003
H. Ritchie, E. Ortiz-Ospina, D. Beltekian, E. Mathieu, J Hasell, B. Macdonald, C. Giattino, C. Appel, L. Rodes-Guirao, M. Roser, Coronavirus Pandemic (COVID-19), 2020. Available at https://www.ourworldindata.org/coronavirus
O. A. Rosso, H. Larrondo, M. T. Martin, A. L. Plastino, M. Fuentes, Distinguishing Noise from Chaos, Phys. Rev. Lett. 99(2007) 154102. Doi: 10.1103/PhysRevLett.99.154102
O. A. Rosso, L. De Micco, H. Larrondo, M. Martin, A. L. Plastino, Generalized statistical complexity measure, Int. J. Bif. and Chaos 20(2010), 775-785. Doi: 10.1142/S021812741002606X
O. A. Rosso, L. De Micco, A. L. Plastino, H. Larrondo, Info-quantifiers’ map-characterization revisited. Physica A 389(2010), 4604-4612. Doi: 10.1016/j.physa.2010.06.055
V. Solovieva, A. Bielinskyia, N. Kharadzjana. Coverage of the coronavirus pandemic through entropy measures, in: CS & SE SW 2020: 3rd Workshop for Young Scientists in Computer Science & Software Engineering, Kryvyi Rih, Ukraine, 2020, 24-42. Available at: https://ceur-ws.org/Vol-2832/paper02.pdf
C. Torrence, G. Compo. A, Practical Guide toWavelet Analysis, Bulletin of the American Meteorological Society, 79(1998): no. 1. Doi: 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
E. Valverde, G. Clemente, P. Arini, V. Vampa, Wavelet-based entropy and complexity to identify cardiac electrical instability in patients post myocardial infarction, Biomedical Signal Processing and Control 69(2021), 102846. Doi: 10.1016/j.bspc.2021.102846
M. Zanin, L. Zunino, O. A. Rosso, D. Papo, Permutation entropy and its main biomedical and econophysics applications: a review, Entropy 14(2012), no. 8, 1553-1577. Doi:10.3390/e14081553
S. Zozor, M. Portesi, P. W. Lamberti, G. M. Bosyk, J. F. Bercher (Eds), Entropies, Divergences, Information, Identities and Inequalities, Entropy Special Issue (2021). Available at https://www.mdpi.com/journal/entropy/special_issues/entro_inqua
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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Derechos de autor 2023 Victoria Vampa, Andrés M. Kowalski, Marcelo Losada, Mariela Portesi, Federico Holik