We present a methodology for generating probabilistic predictions for the Disturbance Storm Time(Dst) geomagnetic activity index. We focus on the One Step Ahead prediction task and use the OMNI hourly resolution data to build our models. Our proposed methodology is based on the technique of Gaussian Process Regression. Within this framework we develop two models; Gaussian Process Autoregressive (GP-AR) and Gaussian Process Autoregressive with eXogenous inputs (GP-ARX). We also propose a criterion to aid model selection with respect to the order of autoregressive inputs. Finally, we test the performance of the GP-AR and GP-ARX models on a set of 63 geomagnetic storms between 1998 and 2006 and illustrate sample predictions with error bars for some of these events.

Dst index, Gaussian process, Machine learning, Probabilistic forecasting, Space weather
dx.doi.org/10.1002/2017SW001627
Space Weather

Chandorkar, M.H, Camporeale, E, & Wing, S.P. (2017). Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach. Space Weather, 15. doi:10.1002/2017SW001627