We introduce the Forecasting Argumentation Framework (FAF), a
novel argumentation framework for forecasting informed by re-
cent judgmental forecasting research. FAFs comprise update frame-
works which empower (human or artificial) agents to argue over
time with and about probability of scenarios, whilst flagging per-
ceived irrationality in their behaviour with a view to improving
their forecasting accuracy. FAFs include three argument types with
future forecasts and aggregate the strength of these arguments to
inform estimates of the likelihood of scenarios. We describe an
implementation of FAFs for supporting forecasting agents.