Statistics 3T: Time Series STATS3018

  • Academic Session: 2018-19
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 3 (SCQF level 9)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes
  • Available to Erasmus Students: Yes

Short Description

This course provides an introduction to the 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.


20 lectures

fortnightly tutorials

2 two-hour practical sessions

Requirements of Entry

The normal requirement is that students should have been admitted to the third year of the Designated Degree programme in Statistics.

Excluded Courses

Time Series [STATS4037]

Time Series (Level M) [STATS5030]


The courses prescribed in the Designated Degree programme to which the student has been admitted.


90 minute, end of course examination (100%)

Main Assessment In: April/May

Course Aims

The aims of this course are:

■ to introduce the concept of a time series, and discuss a range of descriptive methods for identifying features of interest;

■ to present an overview of the approaches for representing trends and seasonality in a time series, and to assess their relative merits;

■ 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 sets in the statistical package R.

Intended Learning Outcomes of Course

By the end of this course students will 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;

■ determine an appropriate model for a data set from the class of ARIMA models;

■ predict future values for a given time series;

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.