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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/3UMEC7B
Repositorysid.inpe.br/mtc-m21c/2020/01.03.16.18   (restricted access)
Last Update2020:01.03.16.18.50 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2020/01.03.16.18.50
Metadata Last Update2022:01.04.01.34.55 (UTC) administrator
DOI10.1016/j.asr.2019.11.011
ISSN0273-1177
1879-1948
Citation KeyGonçalvesEcheFrig:2020:SuCyPr
TitleSunspot cycle prediction using Warped Gaussian process regression
Year2020
MonthJan.
Access Date2024, May 17
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size1153 KiB
2. Context
Author1 Gonçalves, Ítalo G.
2 Echer, Ezequiel
3 Frigo, Everton
Resume Identifier1
2 8JMKD3MGP5W/3C9JH3D
Group1
2 DIDGE-CGCEA-INPE-MCTIC-GOV-BR
Affiliation1 Universidade Federal do Pampa (UNIPAMPA)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Universidade Federal do Rio Grande do Sul (UFRGS)
Author e-Mail Address1 italogoncalves@unipampa.edu.br
2 ezequiel.echer@inpe.br
JournalAdvances in Space Research
Volume65
Number1
Pages677-683
Secondary MarkB1_INTERDISCIPLINAR B1_GEOCIÊNCIAS B1_ENGENHARIAS_IV B1_ENGENHARIAS_III B1_BIODIVERSIDADE B3_CIÊNCIA_DA_COMPUTAÇÃO B4_ASTRONOMIA_/_FÍSICA C_CIÊNCIAS_BIOLÓGICAS_I
History (UTC)2020-01-03 16:18:50 :: simone -> administrator ::
2020-01-03 16:18:51 :: administrator -> simone :: 2019
2020-01-03 16:19:41 :: simone :: 2019 -> 2020
2020-01-03 16:19:41 :: simone -> administrator :: 2020
2022-01-04 01:34:55 :: administrator -> simone :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsSunspot number
Solar cycle
Machine learning
Gaussian process
AbstractSolar 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 25.035.0 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.
AreaCEA
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDGE > Sunspot cycle prediction...
doc Directory Contentaccess
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4. Conditions of access and use
Languageen
Target Filegoncalves_sunspot.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policydenypublisher denyfinaldraft24
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EU29DP
Citing Item Listsid.inpe.br/bibdigital/2013/10.01.22.11 6
sid.inpe.br/mtc-m21/2012/07.13.14.45.47 5
DisseminationWEBSCI; PORTALCAPES; COMPENDEX; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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