A gradient boosting approach to the Kaggle load forecasting competition

Souhaib Ben Taieb, Rob J. Hyndman

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DOI: 10.1016/j.ijforecast.2013.07.005
Published: 2014-04
Container: International Journal of Forecasting
Publisher: Elsevier BV
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1016/j.ijforecast.2013.07.005,
  title = {A gradient boosting approach to the Kaggle load forecasting competition},
  author = {Souhaib Ben Taieb and Rob J. Hyndman},
  year = {2014},
  journal = {International Journal of Forecasting},
  doi = {10.1016/j.ijforecast.2013.07.005},
  url = {https://doi.org/10.1016/j.ijforecast.2013.07.005}
}

RIS

TY  - JOUR
TI  - A gradient boosting approach to the Kaggle load forecasting competition
AU  - Souhaib Ben Taieb
AU  - Rob J. Hyndman
PY  - 2014
JO  - International Journal of Forecasting
DO  - 10.1016/j.ijforecast.2013.07.005
UR  - https://doi.org/10.1016/j.ijforecast.2013.07.005
ER  -

APA

Taieb, S. B., & Hyndman, R. J. (2014). A gradient boosting approach to the Kaggle load forecasting competition. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2013.07.005

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crossref · retrieved 2026-09-25T05:24:24.751Z