Learning to forecast: The probabilistic time series forecasting challenge
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 descr...
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| creator | Bracher, Johannes Koster, Nils Krüger, Fabian Lerch, Sebastian |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2211.16171 |
| format | Article |
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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
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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
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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.</abstract><doi>10.48550/arxiv.2211.16171</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Statistics - Other Statistics |
| title | Learning to forecast: The probabilistic time series forecasting challenge |
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