Machine Learning for the Relationship of High-Energy Electron Flux between GEO and MEO with Application to Missing Values Imputation for Beidou MEO Data

Machine Learning for the Relationship of High-Energy Electron Flux between GEO and MEO with Application to Missing Values Imputation for Beidou MEO Data

We consider the problem of building the relationship of high-energy electron flux between Geostationary Earth Orbit (GEO) and Medium Earth Orbit (MEO). A time-series decomposition technique is first applied to the original data, resulting in trend and detrended part for both GEO and MEO data. Then w...

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Autores principales: Cui Ruifei, Jiang Yu, Tian Chao, Zhang Riwei, Hu Sihui, Li Jiyun
Formato: article
Lenguaje:EN
Publicado: De Gruyter 2021
Materias:
high-energy electron flux
geo/meo
time-series decomposition
random forest
feature extraction
moving block
missing values
Astronomy
QB1-991
Acceso en línea:https://doaj.org/article/5ee8cd57e0584634ba0c05f2cc48a084
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https://doaj.org/article/5ee8cd57e0584634ba0c05f2cc48a084

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