  Solar radio flux along with geomagnetic indices are important indicators of
solar activity and its effects. Extreme solar events such as flares and
geomagnetic storms can negatively affect the space environment including
satellites in low-Earth orbit. Therefore, forecasting these space weather
indices is of great importance in space operations and science. In this study,
we propose a model based on long short-term memory neural networks to learn the
distribution of time series data with the capability to provide a simultaneous
multivariate 27-day forecast of the space weather indices using time series as
well as solar image data. We show a 30-40\% improvement of the root mean-square
error while including solar image data with time series data compared to using
time series data alone. Simple baselines such as a persistence and running
average forecasts are also compared with the trained deep neural network
models. We also quantify the uncertainty in our prediction using a model
ensemble.
