ST304      Half Unit
Time Series and Forecasting

This information is for the 2022/23 session.

Teacher responsible

Dr Yining Chen COL 7.06


This course is available on the BSc in Actuarial Science, BSc in Business Mathematics and Statistics, BSc in Data Science, BSc in Mathematics with Economics and BSc in Mathematics, Statistics and Business. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.

This course is not capped, any student that requests a place and meet the criteria will be given one.


2nd year statistics and probability

Students who have no previous experience in R are required to complete an online pre-sessional R course from the Digital Skills Lab before the start of the course (

Course content

The course introduces the student to the statistical analysis of time series data and simple time series models, and showcase what time series analysis can be useful for. Topics include: autocorrelation; stationarity, trend removal and seasonal adjustment; AR, MA, ARMA, ARIMA; estimation; forecasting; unit root test; introduction to financial time series and the ARCH/GARCH models; basic spectral analysis. The use of R for time series analysis will also be covered.


This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 30 hours across Lent Term. This course includes a reading week in Week 6 of Lent Term.

Formative coursework

Written answers to set problems will be expected on a weekly basis.

Indicative reading

Peter J. Brockwell and Richard A. Davis, Introduction to Time Series and Forecasting

Robert H. Shumway and David S. Stoffer, Time Series Analysis and Its Applications: With R Examples

Christopher Chatfield, The Analysis of Time Series

Ruey S. Tsay, An Introduction to Analysis of Financial Data with R

Peter J. Brockwell and Richard A. Davis, Time Series: Theory and Methods

Christian Francq and Jean-Michel Zakoïan, GARCH Models: Structure, Statistical Inference and Financial Applications


Exam (90%, duration: 2 hours) in the summer exam period.
Coursework (10%).

Student performance results

(2019/20 - 2021/22 combined)

Classification % of students
First 42.2
2:1 28.6
2:2 18.2
Third 4.5
Fail 6.5

Key facts

Department: Statistics

Total students 2021/22: 43

Average class size 2021/22: 11

Capped 2021/22: No

Value: Half Unit

Guidelines for interpreting course guide information

Course selection videos

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.

Personal development skills

  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Commercial awareness
  • Specialist skills