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Empirical Study on the Impact of Different Sets of Parameters of Gradient Boosting Algorithms for Time-Series Forecasting with Lightgbm

AuthID
P-00V-N4Y

Author(s)

Barros, F

Cerqueira, V

Soares, C

Editor(es)

Pham,DN;Theeramunkong,T;Governatori,G;Liu,F

Tipo de Documento

Proceedings Paper

Year published

2021

Publicado

in PRICAI 2021: Trends in Artificial Intelligence - 18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021, Hanoi, Vietnam, November 8-12, 2021, Proceedings, Part I in Lecture Notes in Computer Science, ISSN: 0302-9743

Volume: 13031, Páginas: 454-465

Conference

18Th Pacific Rim International Conference on Artificial Intelligence, Pricai 2021, Date: 8 November 2021 through 12 November 2021

Indexing

Scopus^®

Dblp^®/pt/publications/view/906398

Crossref^®2

Google Scholar^®

Metadata
Fontes

Publication Identifiers

DOI: 10.1007/978-3-030-89188-6_34

DBLP: conf/pricai/BarrosCS21

SCOPUS: 2-s2.0-85119022147

Source Identifiers

ISSN: 0302-9743

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Empirical Study on the Impact of Different Sets of Parameters of Gradient Boosting Algorithms for Time-Series Forecasting with Lightgbm

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Empirical Study on the Impact of Different Sets of Parameters of Gradient Boosting Algorithms for Time-Series Forecasting with Lightgbm

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