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

Abstract

Abstract 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 we predict MEO trend with GEO data using three machine learning models: Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP). Experiment shows that RF gains best performance in all scenarios. Feature extraction analysis demonstrates that the inclusion of lagged features and (possible) ahead features is substantially helpful to the prediction. At last, an application of imputing missing values for MEO data is presented, in which RF model with selected features is used to handle the trend part while a moving block method is for the detrended part.