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arXiv · 2006.03922

The Zoltar forecast archive: a tool to facilitate standardization and storage of interdisciplinary prediction research

Nicholas G Reich·Matthew Cornell·Evan L Ray·Katie House·Khoa Le
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Abstract

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.

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Nicholas G Reich, Matthew Cornell, Evan L Ray, Katie House, Khoa Le. 2020-06-06. The Zoltar forecast archive: a tool to facilitate standardization and storage of interdisciplinary prediction research. https://arxiv.org/abs/2006.03922

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