  Space weather indices are used commonly to drive forecasts of thermosphere
density, which directly affects objects in low-Earth orbit (LEO) through
atmospheric drag. One of the most commonly used space weather proxies, $F_{10.7
cm}$, correlates well with solar extreme ultra-violet (EUV) energy deposition
into the thermosphere. Currently, the USAF contracts Space Environment
Technologies (SET), which uses a linear algorithm to forecast $F_{10.7 cm}$. In
this work, we introduce methods using neural network ensembles with multi-layer
perceptrons (MLPs) and long-short term memory (LSTMs) to improve on the SET
predictions. We make predictions only from historical $F_{10.7 cm}$ values, but
also investigate data manipulation to improve forecasting. We investigate data
manipulation methods (backwards averaging and lookback) as well as multi step
and dynamic forecasting. This work shows an improvement over the baseline when
using ensemble methods. The best models found in this work are ensemble
approaches using multi step or a combination of multi step and dynamic
predictions. Nearly all approaches offer an improvement, with the best models
improving between 45 and 55\% on relative MSE. Other relative error metrics
were shown to improve greatly when ensembles methods were used. We were also
able to leverage the ensemble approach to provide a distribution of predicted
values; allowing an investigation into forecast uncertainty. Our work found
models that produced less biased predictions at elevated and high solar
activity levels. Uncertainty was also investigated through the use of a
calibration error score metric (CES), our best ensemble reached similar CES as
other work.
