  The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden
of COVID-19 in the United States from many contributing teams. We study methods
for building an ensemble that combines forecasts from these teams. These
experiments have informed the ensemble methods used by the Hub. To be most
useful to policy makers, ensemble forecasts must have stable performance in the
presence of two key characteristics of the component forecasts: (1) occasional
misalignment with the reported data, and (2) instability in the relative
performance of component forecasters over time. Our results indicate that in
the presence of these challenges, an untrained and robust approach to
ensembling using an equally weighted median of all component forecasts is a
good choice to support public health decision makers. In settings where some
contributing forecasters have a stable record of good performance, trained
ensembles that give those forecasters higher weight can also be helpful.
