Course Aims and ILOs

On completion of the course students should be able to independently analyse time series data to address any questions of interest. This involves

  • understanding the relevant theory;
  • following an appropriate modelling strategy;
  • and being able to apply the methods to real data using R.

By the end of the course students should be able to:

  • determine whether a time series exhibits any evidence of a trend, seasonality or short-term correlation;
  • define what it means for a time series to be stationary;
  • define the class of ARIMA probability models;
  • determine whether a particular model from the class of ARIMA models is stationary and invertible;
  • derive the mean, variance and autocorrelation function for a particular model from the class of ARIMA models;
  • determine an appropriate model for a data set from the class of ARIMA models;
  • predict future values for a given time series; and
  • use the statistical programming language R to fit an appropriate time series model to a real data set that adequately captures any trend, seasonal variation and short-term correlation.