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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Wavelet variances for heavy-tailed time series

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Autor(es):
Fonseca, R. V. [1] ; Mondal, D. [2] ; Zhang, L. [3]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Estadual Campinas, Campinas, SP - Brazil
[2] Oregon State Univ, Corvallis, OR 97331 - USA
[3] Univ Penn, Philadelphia, PA 19104 - USA
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: ENVIRONMETRICS; v. 30, n. 6 SEP 2019.
Citações Web of Science: 0
Resumo

This paper focuses on wavelet analysis of variability for heavy-tailed time series. Under the assumption that time-series values have finite second but infinite fourth moments, stable asymptotics are derived for wavelet variances across different time scales. These stable asymptotics have a slower rate of convergence than the square root of the sample size and are markedly different from conventional normal asymptotics. Furthermore, the asymptotic results apply even when the time series exhibits long-range dependence. Wavelet variances and stable asymptotics are then used to analyze three streamflows: one in Arizona, one in Connecticut, and one in Illinois. These analyses provide a deeper understanding of streamflow variability at different time scales (e.g., extreme variation at short time scales that are characteristic of heavy rainfall, presence of seasonal variations, and, in one case, some quasi-biennial fluctuations). Furthermore, this paper includes evaluations of local characteristic time scales, a discussion of tail heaviness, computation of Hurst exponents, and some future directions of research. (AU)

Processo FAPESP: 18/06874-6 - Análise por wavelets de grafos temporais
Beneficiário:Rodney Vasconcelos Fonseca
Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado