Sunspot cycle prediction using Warped Gaussian process regression

Autor(es)

Goncalves, Italo G. ^[1] ; Echer, Ezequiel ^[2] ; Frigo, Everton ^{[3, 1]}

Afiliação do(s) autor(es)

^[1] Univ Fed Pampa, Geophys Signals Anal Lab, Bage, RS - Brazil

^[2] Inst Nacl Pesquisas Espaciais, Sao Jose Dos Campos - Brazil

^[3] Univ Fed Rio Grande do Sul, Inst Geociencias, Porto Alegre, RS - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Advances in Space Research; v. 65, n. 1, p. 677-683, JAN 1 2020.
• Citações Web of Science: 5

Resumo

Solar cycle prediction is a key activity in space weather research. Several techniques have been employed in recent decades in order to try to forecast the next sunspot-cycle maxima and time. In this work, the Gaussian process, a machine-learning technique, is used to make a prediction for the solar cycle 25 based on the annual sunspot number 2.0 data from 1700 to 2018. A variation known as Warped Gaussian process is employed in order to deal with the non-negativity constraint and asymmetrical data distribution. Tests using holdout data yielded a root mean square error of 10.0 within 5 years and 25.0-35.0 within 10 years. Simulations using the predictive distribution were performed to account for the uncertainty in the prediction. Cycle 25 is expected to last from 2019 to 2029, with a peak sunspot number about 117 (110 by the median) occurring most likely in 2024. Thus our method predicts that solar Cycle 25 will be weaker than previous ones, implying a continuing trend of declining solar activity as observed in the past two cycles. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 18/21657-1 - Estudo da variabilidade da atividade auroral em rádio da magnetosfera de Júpiter
• Beneficiário: Ezequiel Echer
• Modalidade de apoio: Auxílio à Pesquisa - Regular