The Prediction of the Dst-Index Based on Machine Learning Methods

Abstract

This paper investigates the possibility of predicting the time series of the geomagnetic index Dst. The prediction is based on parameters of the solar wind and interplanetary magnetic field measured at Lagrange point L1 within the Advanced Composition Explorer (ACE) spacecraft experiment using machine learning methods—artificial neural networks: classical perceptrons, recurrent networks of long short-term memory (LSTM), and committees of predictive models. Ultimately, the best results have been obtained using heterogeneous committees based on neural networks of both types.