  Forecasting has emerged as an important component of informed, data-driven
decision-making in a wide array of fields. We introduce a new data model for
probabilistic predictions that encompasses a wide range of forecasting
settings. This framework clearly defines the constituent parts of a
probabilistic forecast and proposes one approach for representing these data
elements. The data model is implemented in Zoltar, a new software application
that stores forecasts using the data model and provides standardized API access
to the data. In one real-time case study, an instance of the Zoltar web
application was used to store, provide access to, and evaluate real-time
forecast data on the order of 10$^7$ rows, provided by over 20 international
research teams from academia and industry making forecasts of the COVID-19
outbreak in the US. Tools and data infrastructure for probabilistic forecasts,
such as those introduced here, will play an increasingly important role in
ensuring that future forecasting research adheres to a strict set of rigorous
and reproducible standards.
