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Solar radio flux along with geomagnetic indices are important indicators ofsolar activity and its effects. Extreme solar events such as flares andgeomagnetic storms can negatively affect the space environment includingsatellites in low-Earth orbit. Therefore, forecasting these space weatherindices 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 thedistribution of time series data with the capability to provide a simultaneousmultivariate 27-day forecast of the space weather indices using time series aswell as solar image data. We show a 30-40\% improvement of the root mean-squareerror while including solar image data with time series data compared to usingtime series data alone. Simple baselines such as a persistence and runningaverage forecasts are also compared with the trained deep neural networkmodels. We also quantify the uncertainty in our prediction using a modelensemble.

Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles