ECTS
4 credits
Component
Faculty of Science
Description
This introductory course on time series, i.e. a series of observations made over time, is an indispensable toolbox for processing this type of data, which is frequently encountered in a wide range of applications: the concentration of a pollutant in the air over time, blood glucose levels over time, sales of a product in a supermarket, stock market prices, etc. This course covers both the mathematical presentation of the concepts and the more technical aspects of implementing the methods. The course covers both the mathematical presentation of the concepts and the more technical aspects of implementing the methods. Numerical illustrations are provided using R software.
Objectives
Master the main concepts of time series modeling. Be able to propose a suitable method for modeling and predicting a time series.
Necessary prerequisites
Analysis, probability and statistics at L3 level.
Recommended prerequisites: first semester of M1 SSD
Knowledge control
CCI + project
Syllabus
- Descriptive analysis of a time series
- ARMA processes, auto-correlograms and partial auto-correlograms.
- Spectral analysis
- Linear prediction: Yule-Walker equations, Durbin-Watson algorithm
- Estimate
- Portmanteau test
Further information
Timetable:
CM: 15h
TD: 15h
TP:
Field :