Study programme 2020-2021Français
Demand Forecasting Methods
Programme component of Master's in Mathematics à la Faculty of Science

Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what special Covid-19 assessment methods are possibly planned for the end of Q3

CodeTypeHead of UE Department’s
contact details
Teacher(s)
US-M1-SCMATH-043-MOptional UEPIRLOT MarcF151 - Mathématique et Recherche opérationnelle
  • SIEBERT Xavier

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français121200033.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-MARO-016Streaming Data Analysis1212000Q1100.00%
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.

Content of UE

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 Assessment for UE in Q1

  • Presentation and/or works
  • Written examination

Q1 UE Assessment Comments

The assessment relies upon the discussion of a report on the analysis and forecasting of a real data set and the evaluation of the student knowledge and understanding of concepts and methods.

Type of Assessment for UE in Q3

  • Presentation and/or works
  • Written examination

Q3 UE Assessment Comments

The assessment relies upon the discussion of a report on the analysis and forecasting of a real data set (if the marks obtained for that work in Term 1 were below 50%) and the evaluation of the student knowledge and understanding of concepts and methods.

Type of Resit Assessment for UE in Q1 (BAB1)

  • Written examination

Q1 UE Resit Assessment Comments (BAB1)

Not applicable

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
I-MARO-016
  • Cours magistraux
  • Travaux pratiques
  • Travaux de laboratoire
  • Etudes de cas

Mode of delivery

AAMode of delivery
I-MARO-016
  • Mixed

Required Reading

AA
I-MARO-016

Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-MARO-016Not applicable

Recommended Reading

AA
I-MARO-016

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-MARO-016Not applicable

Other Recommended Reading

AAOther Recommended Reading
I-MARO-016C. 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

AAGrade Deferrals of AAs from one year to the next
I-MARO-016Authorized
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term
Date de génération : 09/07/2021
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