  Many systems used by society are extremely vulnerable to space weather events
such as solar flares and geomagnetic storms which could potentially cause
catastrophic damage. In recent years, many works have emerged to provide early
warning to such systems by forecasting these events through some proxy, but
these approaches have largely focused on a specific phenomenon. We present a
sequence-to-sequence learning approach to the problem of forecasting global
space weather conditions at an hourly resolution. This approach improves upon
other work in this field by simultaneously forecasting several key proxies for
geomagnetic activity up to 6 hours in advance. We demonstrate an improvement
over the best currently known predictor of geomagnetic storms, and an
improvement over a persistence baseline several hours in advance.
