The use of long short-term memory and gated recurrent unit for predicting the values of geomagnetic indices

In this work, with the help of deep learning, predicting the values of the following geomagnetic indices (GI) is considered: Dst, Kp, AE and Ap. For forecasting we use the architectures are long short-term memory (LSTM) and gated recurrent unit (GRU). For various GI indices, the loss function is analyzed depending on the periodicity of the source data. It has been established that forecasting accuracy increases with decreasing periodicity of the initial data of geomagnetic indices. For the analysis, the following periods of the initial GI data were used: hour, 3 hours, day. For the analysis we used hour, 3 hours and day periods of the initial GI source data.