Real-time emission prediction for ship engine using stacked time-series learning: a transformer-XGBoost hybrid framework

Seunghun Lim, Jungmo Oh

Open source

DOI: 10.1038/s41598-025-24393-x
Published: 2025-11-18
Container: Scientific Reports
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1038/s41598-025-24393-x,
  title = {Real-time emission prediction for ship engine using stacked time-series learning: a transformer-XGBoost hybrid framework},
  author = {Seunghun Lim and Jungmo Oh},
  year = {2025},
  journal = {Scientific Reports},
  doi = {10.1038/s41598-025-24393-x},
  url = {https://doi.org/10.1038/s41598-025-24393-x}
}

RIS

TY  - JOUR
TI  - Real-time emission prediction for ship engine using stacked time-series learning: a transformer-XGBoost hybrid framework
AU  - Seunghun Lim
AU  - Jungmo Oh
PY  - 2025
JO  - Scientific Reports
DO  - 10.1038/s41598-025-24393-x
UR  - https://doi.org/10.1038/s41598-025-24393-x
ER  -

APA

Lim, S., & Oh, J. (2025). Real-time emission prediction for ship engine using stacked time-series learning: a transformer-XGBoost hybrid framework. Scientific Reports. https://doi.org/10.1038/s41598-025-24393-x

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crossref · retrieved 2026-09-27T00:44:38.201Z