Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification

arXiv:2402.17196v1 Announce Type: cross
Abstract: 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 …

abstract arxiv astro-ph.im bayesian bayesian deep learning cs.lg deep learning deep learning framework framework graph graph neural network index learn long short-term memory memory network neural network novel parameters patterns prediction quantification solar type uncertainty wind