The use of sequential recurrent neural filters in forecasting the index for the strong magnetic storm of autumn 2003

Neural based geomagnetic forecasting literature has heavily relied upon non-sequential algorithms for estimation of model parameters. This paper proposes sequential Bayesian recurrent neural filters for online forecasting of the D s t  index. Online updating of the RNN parameters allows for newly arrived observations to be included into the model. The online RNN filters are compared to two (non-sequentially trained) models on a severe double storm that has so far been difficult to forecast. It is shown that the proposed models can significantly reduce forecast errors over non-sequentially trained recurrent neural models.