A negative Dst value means that the Earth’s magnetic field is weakened, which happens during storms. It has been used to characterize the size and intensity of a geomagnetic storm. To our knowledge, this is the first time that Bayesian deep learning has been used for Dst index forecasting.ĪB - The disturbance storm time (Dst) index is an important and useful measurement in space weather research. Furthermore, the Dst Transformer can produce both data and model uncertainty quantification results, which can not be done by the existing methods. Experimental results show that the proposed Dst Transformer outperforms related machine learning methods in terms of the root mean square error and R-squared. 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. 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. N2 - The disturbance storm time (Dst) index is an important and useful measurement in space weather research. We acknowledge the use of NASA/GSFC’s Space Physics Data Facility’s OMNIWeb service and OMNI data. T1 - Forecasting the Disturbance Storm Time Index with Bayesian Deep Learning To our knowledge, this is the first time that Bayesian deep learning has been used for Dst index forecasting.", A negative Dst value means that the Earths magnetic field is weakened, which happens during storms. To our knowledge, this is the first time that Bayesian deep learning has been used for Dst index forecasting.Ībstract = "The disturbance storm time (Dst) index is an important and useful measurement in space weather research. The disturbance storm time (Dst) index is an important and useful measurement in space weather research.
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