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.