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Scientific Data


Forecasting has emerged as an important component of informed, data-driven decision-making in a wide array of felds. We introduce a new data model for probabilistic predictions that encompasses a wide range of forecasting settings. This framework clearly defnes 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 108 rows, provided by over 40 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.





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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.