Learning to Forecast: The Probabilistic Time Series Forecasting Challenge
Johannes Bracher,
Nils Koster,
Fabian Krüger and
Sebastian Lerch
The American Statistician, 2024, vol. 78, issue 1, 115-127
Abstract:
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.
Date: 2024
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/00031305.2023.2199800 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:amstat:v:78:y:2024:i:1:p:115-127
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UTAS20
DOI: 10.1080/00031305.2023.2199800
Access Statistics for this article
The American Statistician is currently edited by Eric Sampson
More articles in The American Statistician from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().