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Time series


Time Series 

  • Introduction and Descriptive Analysis
  • Random Modeling of Time Series
  • Statistical Inference of Stationary processes of order 2
  • ARMA and ARIMA Models



Time series :

At the end of this lecture, the student should have acquired the following skills, as well theoretically than practically with the R statistical Software.

  • Estimate or eliminate the trend and/or the seasonality of a time series
  • Study the stationnarity of a time series
  • Calculate and estimate the autocorrelogram and the autocorrelograms (total and partial) of a stationary process
  • Study and/or adjust an ARMA (or ARIMA) model on a stationary time series
  • Carry an optimal linear forecast of an ARMA process


Needed prerequisite

Probability and Statistics (I2MIMT31]

Statistics (I3MIMT41]

Elements of Statistical Modelling (I4MMMS71)

Form of assessment

The evaluation of outcome prior learning is made as a continuous training during the semester. According ot the teaching, the assessment will be different: as a written exam, an oral exam, a record, a written report, peers review...