Recalibrating probabilistic forecasts of epidemics
Fig 9
Mean log score for the two different approaches to recalibrating the FluSight ensemble forecaster, with C-E and E-C reflecting the order of recalibration and ensembling.
Both the C-E and E-C models outperform the original ensemble (with no recalibration), but ensembling followed by recalibration performs best. By viewing forecast performance as a function of time, recalibration increases performance as much as roughly two days’ time would.