Quality of Prediction of Daily Relativistic Electrons Flux at Geostationary Orbit by Machine Learning Methods

Abstract

This study presents the results of prediction 1–3 days ahead for the daily maximum of hourly average values of relativistic electrons flux (E > 2 MeV) in the outer radiation belt of the Earth. The input physical variables were geomagnetic indexes, interplanetary magnetic field, solar wind velocity and proton density, special ultra-low frequency (ULF) indexes and hourly average values of relativistic electron flux. The phase-space for each physical component was reconstructed by time delay vectors with their own different embedding dimensions, and all of these vectors were concatenated. Next, various adaptive models were trained on this multivariate dataset. The following models were used for prediction: multi-dimensional autoregressive model, ensembles of decision trees within bagging approach, artificial neural networks of multi-layer perceptron type. The obtained results are analyzed and compared to the results of similar predictions by other authors. The best prediction quality was demonstrated by ensembles of decision trees. Also it has been demonstrated that using embedding depth based on autocorrelation function significantly improves prediction quality for one day prediction horizon.