  A common technique to reduce model bias in time-series forecasting is to use
an ensemble of predictive models and pool their output into an ensemble
forecast. In cases where each predictive model has different biases, however,
it is not always clear exactly how each model forecast should be weighed during
this pooling. We propose a method for pooling that performs a weighted average
over candidate model forecasts, where the weights are learned by an
attention-based ensemble pooling model. We test this method on two time-series
forecasting problems: multi-step forecasting of the dynamics of the
non-stationary Lorenz `63 equation, and one-step forecasting of the weekly
incident deaths due to COVID-19. We find that while our model achieves
excellent valid times when forecasting the non-stationary Lorenz `63 equation,
it does not consistently perform better than the existing ensemble pooling when
forecasting COVID-19 weekly incident deaths.
