Machine‐Learning Research in the Space Weather Journal: Prospects, Scope, and Limitations. Issue 12 (16th December 2021)

• Record Type: Journal Article
• Authors: Lugaz, Noé; Liu, Huixin; Hapgood, Mike; Morley, Steven
• Is Part Of: Space weather. Volume 19:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-16
• Subjects: machine learning -- editorial -- forecasting; Space environment -- Periodicals; 551.509992
• DOI: 10.1029/2021SW003000
• Languages: English
• ISSNs: 1542-7390

Abstract

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. Plain Language Summary: 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. Key Points: 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 We emphasize that papers focusing on a forecasting technique must present a substantial improvement and comparison over current techniques