Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with
  Uncertainty Quantification
Yasser Abduallah Khalid A. Alobaid Jason T. L. Wang Haimin Wang Vania K. Jordanova Vasyl Yurchyshyn Huseyin Cavus Ju Jing
We propose a novel deep learning framework, named SYMHnet, which employs a
graph neural network and a bidirectional long short-term memory network to
cooperatively learn patterns from solar wind and interplanetary magnetic field
parameters for short-term forecasts of the SYM-H index based on 1-minute and
5-minute resolution data. SYMHnet takes, as input, the time series of the
parameters' values provided by NASA's Space Science Data Coordinated Archive
and predicts, as output, the SYM-H index value at time point t + w hours for a
given time point t where w is 1 or 2. By incorporating Bayesian inference into
the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty
and epistemic (model) uncertainty when predicting future SYM-H indices.
Experimental results show that SYMHnet works well at quiet time and storm time,
for both 1-minute and 5-minute resolution data. The results also show that
SYMHnet generally performs better than related machine learning methods. For
example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the
FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting
SYM-H indices (1 hour in advance) in a large storm (SYM-H = -393 nT) using
5-minute resolution data. When predicting the SYM-H indices (2 hours in
advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the
FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for
both data and model uncertainty quantification, whereas the related methods
cannot.