Machine-Learning Research in the Space Weather Journal: Prospects, Scope, and Limitations

Noé Lugaz, Huixin Liu, Mike Hapgood, Steven Morley

Research output: Contribution to journalEditorialpeer-review

Abstract

Manuscripts based on machine-learning techniques have significantly increased in Space Weather over the past few years. We discuss which manuscripts are within the journal's scope and emphasize that manuscripts focusing purely on a forecasting technique (rather than on understanding and forecasting a phenomenon) must correspond to a substantial improvement over the current state-of-the-art techniques and present this comparison. All manuscripts shall include information about data preparation, including splitting of data between training, validation and testing sets. The software and/or algorithms used for to develop the machine-learning technique should be included in a repository at the time of submission. Comparison with published results using other methods must be presented, and uncertainties of the forecast results must be discussed.

Original languageEnglish
Article numbere2021SW003000
JournalSpace Weather
Volume19
Issue number12
DOIs
Publication statusPublished - Dec 2021

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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