While combining is useful whenever more than one forecasts for the outcome are available, the approach is particularly valuable if Many forecasts from evidence-based methods are available. The forecasts draw.
One intuitive explanation as to why combining improves accuracy is that it enables forecasters to use more information, and to do so in an objective manner. Moreover, bias exists both.
The rationale behind combining forecasts first within and then across component methods is to equalize the impact of each component method, regardless of whether a component included many forecasts or.
A widespread concern when combining forecasts is the question of how best to weight the components, and many scholars have proposed different methods for doing so. However, an early review.
The PollyVote is based on the principle of combining forecasts. That is, PollyVote combines forecasts from different forecasting methods, the so-called component methods, each of which rely on different data..