ECTS
4 credits
Training structure
Faculty of Science
Description
This introductory course on time series, i.e., a sequence of observations made over time, provides an essential toolkit for processing this type of data, which is frequently encountered in a wide range of applications: pollutant concentration in the air over time, blood glucose levels over time, sales of a product in a supermarket, stock market prices, etc. This course focuses both on the mathematical presentation of concepts and on the more technical aspects of implementing methods. Numerical illustrations are provided using R software.
Objectives
Master the main concepts for time series modeling. Be able to propose a suitable method for modeling and predicting a time series.
Teaching hours
- Time Series - CMLecture3 p.m.
- Time series - TutorialsTutorials3 p.m.
Mandatory prerequisites
Analysis, probability, and statistics at the L3 level.
Recommended prerequisites: first semester of M1 SSD
Knowledge assessment
CCI + project
Syllabus
- Descriptive analysis of a time series
- ARMA process, autocorrelograms, and partial autocorrelograms.
- Spectral analysis
- Linear prediction: Yule-Walker equations, Durbin-Watson algorithm
- Estimate
- Testing the portmanteau
Additional information
Hours per week:
Lectures: 15 hours
Tutorials: 15 hours
Practical work:
Fieldwork: