  Geomagnetic storms (GS) occur when solar winds disrupt Earth's magnetosphere.
GS can cause severe damages to satellites, power grids, and communication
infrastructures. Estimate of direct economic impacts of a large scale GS
exceeds $40 billion a day in the US. Early prediction is critical in preventing
and minimizing the hazards. However, current methods either predict several
hours ahead but fail to identify all types of GS, or make predictions within
short time, e.g., one hour ahead of the occurrence. This work aims to predict
all types of geomagnetic storms reliably and as early as possible using big
data and machine learning algorithms. By fusing big data collected from
multiple ground stations in the world on different aspects of solar
measurements and using Random Forests regression with feature selection and
downsampling on minor geomagnetic storm instances (which carry majority of the
data), we are able to achieve an accuracy of 82.55% on data collected in 2021
when making early predictions three hours in advance. Given that important
predictive features such as historic Kp indices are measured every 3 hours and
their importance decay quickly with the amount of time in advance, an early
prediction of 3 hours ahead of time is believed to be close to the practical
limit.
