Papers › Learning to forecast: The probabilistic time series forecasting challenge
Learning to forecast: The probabilistic time series forecasting challenge
Johannes Bracher, Nils Koster, Fabian Krüger, Sebastian Lerch
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We report on a course project in which students submit weekly probabilistic forecasts of two weather variables and one financial variable. This real-time format allows students to engage in practical forecasting, which requires a diverse set of skills in data science and applied statistics. We describe the context and aims of the course, and discuss design parameters like the selection of target variables, the forecast submission process, the evaluation of forecast performance, and the feedback provided to students. Furthermore, we describe empirical properties of students' probabilistic forecasts, as well as some lessons learned on our part.
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