New Findings From Explainable SYM‐H Forecasting Using Gradient Boosting Machines-Reference-Cited by-同舟云学术

New Findings From Explainable SYM‐H Forecasting Using Gradient Boosting Machines

Published:2022-08 Issue:8 Volume:20 Page:
ISSN:1542-7390
Container-title:Space Weather
language:en
Short-container-title:Space Weather

Author:

Iong Daniel¹^ORCID,Chen Yang¹^ORCID,Toth Gabor²^ORCID,Zou Shasha²^ORCID,Pulkkinen Tuija²^ORCID,Ren Jiaen²^ORCID,Camporeale Enrico³⁴^ORCID,Gombosi Tamas²^ORCID

Affiliation:

1. Department of Statistics University of Michigan Ann Arbor MI USA

2. Department of Climate and Space Sciences and Engineering University of Michigan Ann Arbor MI USA

3. CIRES University of Colorado Boulder CO USA

4. NOAA Space Weather Prediction Center Boulder CO USA

Funder

National Aeronautics and Space Administration

Publisher

American Geophysical Union (AGU)

Subject

Atmospheric Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021SW002928

Reference69 articles.

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3. Bailey R. L. Reiss M. A. Arge C. N. Möstl C. Owens M. J. Amerstorfer U. V. et al. (2021).Using gradient boosting regression to improve ambient solar wind model predictions. arXiv:2006[astro‐ph physics:physics].12835.

4. Forecasting of SYMH and ASYH indices for geomagnetic storms of solar cycle 24 including St. Patrick’s day, 2015 storm using NARX neural network

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