Exploring Three Recurrent Neural Network Architectures for Geomagnetic Predictions

Three different recurrent neural network (RNN) architectures are studied for the prediction of geomagnetic activity. The RNNs studied are the Elman, Gated Recurrent Unit (GRU), and Long-Short Term Memory (LSTM). The RNNs take solar wind data as inputs to predict the Dst index. The Dst index summarises complex geomagnetic processes into a single time series. The models are trained and tested using 5-fold cross-validation based on the hourly-resolution OMNI dataset using data from the years 1995 to 2015. The inputs are solar wind plasma (particle density and speed) and vector magnetic fields, and time-of-year and time-of-day. The RNNs are regularised using early stopping and dropout. We find that both the GRU and LSTM models perform better than the Elman model, however, we see no significant difference in performance between GRU and LSTM. RNNs with dropout requires more weights to reach the same validation error as networks without dropout. However, the gap between training error and validation error becomes smaller when dropout is applied, reducing overfitting and improving generalization. Another advantage in using dropout is that it can be applied during prediction to provide confidence limits on the predictions. The confidence limits increase with increasing Dst magnitude, a consequence of the less populated input-target space for events with large Dst values thereby increasing the uncertainty in the estimates. The best RNNs have test set RMSE of 8.8 nT, bias close to zero, and coefficient-of-determination (R2) of 0.83.