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Block
Probabilistic Time Series Forecasting Challenge [T-WIWI-111387]
Type
Examination of another typeCredits
4.5Recurrence
IrregularVersion
2Responsible
Organisation
- KIT-Fakultät für Wirtschaftswissenschaften
Part of
- Module Econometrics and Statistics II | Industrial Engineering and Management (M.Sc.)
- Module Econometrics and Statistics I | Industrial Engineering and Management (M.Sc.)
- Module Econometrics and Statistics II | Economics Engineering (M.Sc.)
- Module Econometrics and Statistics I | Economics Engineering (M.Sc.)
- Module Econometrics and Statistics II | Digital Economics (M.Sc.)
- Module Econometrics and Statistics I | Digital Economics (M.Sc.)
- Module Statistics & Econometrics | Digital Economics (M.Sc.)
- Module Econometrics and Statistics II | Information Systems (M.Sc.)
- Module Econometrics and Statistics I | Information Systems (M.Sc.)
- Module Econometrics and Statistics II | Information Engineering and Management (M.Sc.)
- Module Econometrics and Statistics I | Information Engineering and Management (M.Sc.)
- Module Econometrics and Statistics II | Economathematics (M.Sc.)
- Module Econometrics and Statistics I | Economathematics (M.Sc.)
Events
Course Number | Name | SWS | Type |
---|---|---|---|
WS22 2500080 | Probabilistic Time Series Forecasting Challenge | 2 | exercise (Ü) |
WS23 2500080 | Probabilistic Time Series Forecasting Challenge | 2 | exercise (Ü) |
WS23 2500081 | Probabilistic Time Series Forecasting Challenge | project (PRO) | |
WS21 00080 | Probabilistic Time Series Forecasting Challenge | project (PRO) | |
WS22 2500081 | Probabilistic Time Series Forecasting Challenge | project (PRO) |
Exams
Course Number | Name | Appointments |
---|---|---|
WS22 2500081 | Probabilistic Time Series Forecasting Challenge | |
WS22 2500081 | Probabilistic Time Series Forecasting Challenge | |
WS22 2500081 | Probabilistic Time Series Forecasting Challenge |
Competence Certificate
Alternative exam assessment. Necessary conditions to pass the course:
- Weekly submission of statistical forecasts during the semester (excluding the Christmas break),
- Submission of a final report (10-15 pages) at the end of the semester, describing the forecasting methods and their statistical evaluation.
Grading is based on the final report.
Prerequisites
Good methodological knowledge in statistics and data science.
Good knowledge in applied data analysis, incl. programming skills in R, Python or similar.
Knowledge of time series analysis is helpful, but not required.
Annotation
The course is limited in participation. Participants will be selected via the WIWI portal.