 | Study programme 2023-2024 | Français | |
 | Demand Forecasting Methods | ||
Programme component of Master's in Mathematics (MONS) (day schedule) à la Faculty of Science |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-M1-SCMATH-043-M | Optional UE | SIEBERT Xavier | F151 - Mathématique et Recherche opérationnelle |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Français | 12 | 12 | 0 | 0 | 0 | 3 | 3.00 | 1st term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| I-MARO-016 | Streaming Data Analysis | 12 | 12 | 0 | 0 | 0 | Q1 | 100.00% |
| Programme component |
|---|
Objectives of Programme's Learning Outcomes
- Have integrated and elaborate mathematical knowledge.
- Mobilise the Bachelor's course in mathematics to address complex issues and have profound mathematical expertise to complement the knowledge developed in the Bachelor's course.
- Carry out major projects.
- Give constructive criticism on the quality and progress of a project.
- Present the objectives and results of a project orally and in writing.
- Apply innovative methods to solve an unprecedented problem in mathematics or within its applications.
- Appropriately make use of computer tools, as required by developing a small programme.
- Communicate clearly.
- Communicate the results of mathematical or related fields, both orally and in writing, by adapting to the public.
- make a structured and reasoned presentation of the content and principles underlying a piece of work, mobilised skills and the conclusions it leads to.
- Adapt to different contexts.
- Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.
Learning Outcomes of UE
After this UE, the student will master a methodology for analysing time series and forecasting. They will be able to assess the quality of the model as well as the precision of the forecasts.
UE Content: description and pedagogical relevance
Content of the sole AA included in this UE
Prior Experience
Knowledge and practical experience in the analysis of statistical data (descriptive statistics, tests and estimation, linear regression)
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| I-MARO-016 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| I-MARO-016 |
|
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| I-MARO-016 | Not applicable |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| I-MARO-016 | Not applicable |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| I-MARO-016 | C. Chatfield, The analysis of time series, Chapman and Hall, 1989 G. Mélard, Méthodes de prévision à court terme, Editions de l'Université Libre de Bruxelles et Editions Ellipses, 1990 |
Grade Deferrals of AAs from one year to the next
| AA | Grade Deferrals of AAs from one year to the next |
|---|---|
| I-MARO-016 | Authorized |
Term 1 Assessment - type
| AA | Type(s) and mode(s) of Q1 assessment |
|---|---|
| I-MARO-016 |
|
Term 1 Assessment - comments
| AA | Term 1 Assessment - comments |
|---|---|
| I-MARO-016 | Written exam: knowledge and understanding of the theory Report on the study of a time series using a statistical software and a discussion of the report with the student |
Resit Assessment - Term 1 (B1BA1) - type
| AA | Type(s) and mode(s) of Q1 resit assessment (BAB1) |
|---|---|
| I-MARO-016 |
|
Term 3 Assessment - type
| AA | Type(s) and mode(s) of Q3 assessment |
|---|---|
| I-MARO-016 |
|
Term 3 Assessment - comments
| AA | Term 3 Assessment - comments |
|---|---|
| I-MARO-016 | Same as Term 1. |