Time series

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 large number of applications: concentration of a pollutant in the air over time, blood glucose levels over time, sales of a product in a department store, stock market prices, etc. This course focuses on both the mathematical presentation of the concepts and the more technical aspects of the implementation of the methods. Numerical illustrations are proposed with the R software.

Master the main concepts for modeling time series. Be able to propose a suitable method for modeling and predicting a time series.

Analysis, probability and statistics at the L3 level.




Recommended prerequisites: first semester of M1 SSD

 CCI + project

• 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 
• Test of the portmanteau

Hourly volumes:
CM : 15h
TD : 15h
TP :
Field :