Block

Probabilistic Time Series Forecasting Challenge [T-WIWI-111387]

Type

Examination of another type

Credits

4.5

Recurrence

Irregular

Version

Responsible

Prof. Dr. Fabian Krüger

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.