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Filtering by: Creator Camporeale, Enrico Creator Gombosi, Tamas I. I. Creator Iong, Daniel Creator Ren, Jiaen Language Python

Results for "New Findings from Explainable SYM-H Forecasting using Gradient Boosting Machines"

Creator:

Iong, Daniel, Chen, Yang, Toth, Gabor, Zou, Shasha, Pulkkinen, Tuija I., Ren, Jiaen, Camporeale, Enrico, and Gombosi, Tamas I. I.

Description:

In this work, we trained gradient boosted trees using XGBoost to predict the SYM-H forecasting using different combinations of solar wind and interplanetary magnetic field (IMF) parameters. Data are in csv and Python pickle formats.

Keyword:

SYM-H forecasting

Citation to related publication:

Iong, D., Y. Chen, G. Toth, S. Zou, T. I. Pulkkinen, J. Ren, E. Camporeale, and T. I. Gombosi, New Findings from Explainable SYM-H Forecasting using Gradient Boosting Machines, Space Weather,11, accepted, 2022. https://doi.org/10.1002/essoar.10508063.3

Discipline:

Science