Forecast evaluation is inevitably connected with the need to store actuals and forecasts for further analysis. The issue gets complicated when it comes to handling rolling-origin forecasts calculated for many series over multiple horizons. In designing forecast data formats it is important to provide access to all the variables required for exploratory analysis and performance measurement. We show that existing approaches used to store forecast data are not always applicable for implementing reliable cross validation techniques. Here we propose flexible yet simple data schemas allowing the storage and exchange of actuals, forecasts, and additional variables of interest. We also demonstrate how various forecast evaluation tools can be implemented based on the schemas proposed.
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[v1] 2019-03-19 12:18:27
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