Fiche détaillée d'un cours


Voir la fiche établissement



IESEG School of Management ( IÉSEG )

Code Cours :



Niveau Année de formation Période Langue d'enseignement 
MSc in Big Data Analytics for Business1S2English
Professeur(s) responsable(s)F.VAN DEN BOSSCHE
Intervenant(s)F.VAN DEN BOSSCHE

Pré requis

Algebra, inferential statistics, including (practical) knowledge of distributions, hypothesis testing, confidence intervals and the foundations of regression modeling

Objectifs du cours

At the end of the course, the student should be able to:
• Master the forecasting process, its data considerations and business implementation strategies.
• Apply statistical and econometric methods (modeling, estimation, interpretation, forecasting) to obtain forecasts in practical settings in business and economics.
• Understand the statistical background of the methods commonly used for forecasting in business and economics, and assess the appropriateness of the methods for specific problems.
• Build econometric forecasting models using real data into a dedicated econometric software package and interpret the output correctly, including the managerial consequences of the obtained results.
• Communicate about an econometric forecasting analysis, using appropriate scientific jargon.

Contenu du cours

• Forecasting in business and economics
• Basic tools for forecasting
• Exponential smoothing
• Time series decomposition
• ARIMA models
• Forecasting in practice

Modalités d'enseignement

Organisation du cours

TypeNombre d'heuresRemarques
Face to face
Interactive class16,00   Interactive course include presentation of the theoretical concepts and worked out examples
Coaching8,00   Each class includes hands-on activitities, in which students make exercises using forecasting software under guidance of the lecturer
Independent work
E-Learning8,00   We use an online book (Forecasting: principles and practice) written by Rob Hyndman and George Athanasopoulos. Students can re-read the chapters covered in class to improve their own understanding of the material.
Independent study
Group Project8,00   Students prepare exercises at home (and during coaching sessions in class) that will be discussed during the next lecture.
Estimated personal workload35,00  
Charge de travail globale de l'étudiant75,00  

Méthodes pédagogiques

  • E-learning
  • Project work
  • Interactive class
  • Coaching


(1) During lectures, four smaller exerices are provided, and students are expected to hand in a solution after the lectures (deadline will be communicated). This part counts for 20%.
(2) Students individually prepare an empirical paper in which two recent and relevant time series are forecasted, using the techniques that have been discussed during the lectures (deadline will be communicated). This part counts for 80%.

Type de ContrôleDuréeNombrePondération
Individual Project0,00180,00
Written Report4,00120,00
TOTAL     100,00


  • Hyndman, R.J. and Athanasopoulos, G. (2013). Forecasting: principles and practice. Accessed on 2015-12-22. -

* Informations non contractuelles et pouvant être soumises à modification
Vidéo : Un campus à vivre
Notre chaîne Youtube