This course introduces statistical modelling of time series data. The course focuses on the three main areas; (i) modelling trends and seasonal patterns; (ii) modelling short-term correlation; and (iii) predicting observations at future points in time.

Lectures: 20

Tutorials: 4

Practical: 2, 2-hour computer lab sessions

Time Series [STATS4037]

120-minute, end-of-course examination (100%)

To introduce the concept of a time series and discuss a range of descriptive methods for identifying features of interest.

To present a range of approaches for representing trends and seasonality in a time series, and to assess their relative merits.

To describe the theoretical properties of commonly used time series models.

To describe a range of approaches for predicting future values of a time series.

To show how to apply the techniques from the course to real time series data in statistical programming languages.

By the end of this course students will be able to:

■ Determine characteristics of a time series including trends, seasonality, short-term correlation, and stationarity;

■ Formulate the class of ARIMA probability models, estimate their parameters, and validate model assumptions;

■ Predict future values for a given time series;

■ Define and derive properties of models for time series with abrupt changes of behaviour;

■ Use a statistical programming language to implement time series methods for real data sets.