  The disturbance storm time (Dst) index is an important and useful measurement
in space weather research. It has been used to characterize the size and
intensity of a geomagnetic storm. A negative Dst value means that the Earth's
magnetic field is weakened, which happens during storms. In this paper, we
present a novel deep learning method, called the Dst Transformer, to perform
short-term, 1-6 hour ahead, forecasting of the Dst index based on the solar
wind parameters provided by the NASA Space Science Data Coordinated Archive.
The Dst Transformer combines a multi-head attention layer with Bayesian
inference, which is capable of quantifying both aleatoric uncertainty and
epistemic uncertainty when making Dst predictions. Experimental results show
that the proposed Dst Transformer outperforms related machine learning methods
in terms of the root mean square error and R-squared. Furthermore, the Dst
Transformer can produce both data and model uncertainty quantification results,
which can not be done by the existing methods. To our knowledge, this is the
first time that Bayesian deep learning has been used for Dst index forecasting.
