Publications

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(s)

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

Document Type

Proceedings Paper

Year published

2021

Published

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, Pages: 454-465

Conference

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

Indexing

Scopus^®

Dblp^®/en/publications/view/906398

Crossref^®2

Google Scholar^®

Metadata
Sources

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

Export Publication Metadata

Marked List

Add to Marked List

Info

At this moment we don't have any links to full text documens.

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

Useful Links

Get Help

Federated Authentication (Click on the image)

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

Useful Links

Get Help