The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research
Open Access
- 11 February 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Scientific Data
- Vol. 8 (1), 1-11
- https://doi.org/10.1038/s41597-021-00839-5
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 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.Funding Information
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35GM119582, R35GM119582, R35GM119582, R35GM119582)
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences
- U.S. Department of Health & Human Services | Centers for Disease Control and Prevention (1U01IP001122)
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences
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