Event
[WS2100080]
Lecturers
Organisation
- KIT-Fakultät für Wirtschaftswissenschaften
Part of
- Brick Probabilistic Time Series Forecasting Challenge | Industrial Engineering and Management (M.Sc.)
- Brick Probabilistic Time Series Forecasting Challenge | Economics Engineering (M.Sc.)
- Brick Probabilistic Time Series Forecasting Challenge | Information Systems (M.Sc.)
- Brick Probabilistic Time Series Forecasting Challenge | Information Engineering and Management (M.Sc.)
- Brick Probabilistic Time Series Forecasting Challenge | Economathematics (M.Sc.)
Note
Statistical forecasts are relevant across all fields of society. In this data science project, students make, evaluate and communicate their own statistical forecasts in a real-time setting. We consider probabilistic forecasts that involve a measure of uncertainty in addition to a point forecast. Students are asked to make forecasts of several real-world time series (including energy demand and the DAX stock market index). Historical data on all series are available from public sources that are updated as time proceeds. While the time series differ from each other in important ways, statistical methods can meaningfully be used for prediction in all cases. We focus on quantile forecasts which are useful to measure forecast uncertainty in a relatively simple way.