Open AccessMachine‐Learning Research in the Space Weather Journal: Prospects, Scope, and Limitations
Author(s) -
Lugaz Noé,
Liu Huixin,
Hapgood Mike,
Morley Steven
Publication year - 2021
Publication title -
space weather
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.254
H-Index - 56
ISSN - 1542-7390
DOI - 10.1029/2021sw003000
Subject(s) - scope (computer science) , computer science , space (punctuation) , data science , phenomenon , software , machine learning , artificial intelligence , epistemology , programming language , philosophy , operating system
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.