Close

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemtc-m16c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGPDW34P/43PLBM8
Repositorysid.inpe.br/mtc-m16c/2020/12.14.11.52
Last Update2020:12.14.11.52.42 (UTC) banon
Metadata Repositorysid.inpe.br/mtc-m16c/2020/12.14.11.52.42
Metadata Last Update2023:01.30.13.08.20 (UTC) administrator
ISSN2179-4847
Citation KeyAlvesPere:2020:MeFrIm
TitleA Meta-Learning Framework for Imputing Missing Values in Weather Time Series
FormatOn-line
Year2020
Access Date2024, Apr. 25
Secondary TypePRE CN
Number of Files1
Size400 KiB
2. Context
Author1 Alves, Vinícius H. A.
2 Pereira, Marconi A.
Affiliation1 Universidade Federal de Sao Joao del-Rei (UFJR)
2 Universidade Federal de Sao Joao del-Rei (UFJR)
Author e-Mail Address1 viniciushaalves97@gmail.com
2 marconi@ufsj.edu.br
EditorCarneiro, Tiago Garcia de Senna (UFOP)
Felgueiras, Carlos Alberto (INPE)
e-Mail Addresslubia@dpi.inpe.br
Conference NameSimpósio Brasileiro de Geoinformática, 21 (GEOINFO)
Conference LocationOn-line
Date30 nov. a 03 dez. 2020
PublisherInstituto Nacional de Pesquisas Espaciais (INPE)
Publisher CitySão José dos Campos
Pages58-69
Book TitleAnais
Tertiary TypeFull Paper
History (UTC)2020-12-14 11:52:42 :: lubia@dpi.inpe.br -> administrator ::
2021-01-04 22:29:47 :: administrator -> simone :: 2020
2021-01-05 19:22:53 :: simone -> administrator :: 2020
2021-02-13 03:02:47 :: administrator -> banon :: 2020
2021-02-13 03:12:19 :: banon -> administrator :: 2020
2023-01-30 13:08:20 :: administrator -> banon :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
AbstractThis paper describes an application of a meta-learning framework based on bagged trees. The proposed tool is used to estimate missing weather values in time series. The framework combines 8 different models of bagged trees that were optimized by a meta-learning algorithm. One of those 8 mod- els was trained using only the date and each one of the remaining seven was calibrated with one weather parameter (max. temperature, min. temperature, insolation, among others), in addition to the respective date. The results show improvements in accuracy of the predicted values, achieving values such as R2 = 0.94.
AreaSRE
TypeGeoinformação
Arrangement 1urlib.net > BDMCI > Fonds > GEOINFO > XXI GEOINFO > A Meta-Learning Framework...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > XXI GEOINFO > A Meta-Learning Framework...
Arrangement 3urlib.net > BDMCI > Fonds > GEOINFO > Coleção GEOINFO > A Meta-Learning Framework...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPDW34P/43PLBM8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPDW34P/43PLBM8
Languageen
Target Filep6.pdf
User Grouplubia@dpi.inpe.br
Reader Groupadministrator
banon
simone
Visibilityshown
Copyright Licenseurlib.net/www/2012/11.12.15.19
Rightsholderoriginalauthor yes
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPDW34P/43PRNME
8JMKD3MGPDW34P/48F29JE
Citing Item Listsid.inpe.br/mtc-m16c/2020/12.15.17.26 3
Host Collectionsid.inpe.br/mtc-m18@80/2008/03.17.15.17
6. Notes
Empty Fieldsarchivingpolicy archivist callnumber contenttype copyholder creatorhistory descriptionlevel dissemination doi edition group isbn keywords label lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization parameterlist parentrepositories previousedition previouslowerunit progress project readpermission resumeid schedulinginformation secondarydate secondarykey secondarymark serieseditor shorttitle sponsor tertiarymark url versiontype volume
7. Description control
e-Mail (login)banon
update 


Close