ST422      Half Unit
Time Series

This information is for the 2020/21 session.

Teacher responsible

Prof Wai-Fung Lam


This course is compulsory on the MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (LSE and Fudan) and MSc in Statistics (Financial Statistics) (Research). This course is available on the MSc in Applicable Mathematics, MSc in Data Science, MSc in Econometrics and Mathematical Economics, MSc in Financial Mathematics, MSc in Marketing, MSc in Operations Research & Analytics, MSc in Quantitative Methods for Risk Management, MSc in Statistics and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.


Good undergraduate knowledge of statistics and probability.

Course content

A broad introduction to statistical time series analysis for postgraduates: what time series analysis can be useful for; autocorrelation; stationarity; causality;  basic time series models: AR, MA, ARMA; ARCH and GARCH models for financial time series; trend removal and seasonal adjustment; invertibility; spectral analysis; estimation; forecasting. We will also discuss nonstationarity and multivariate time series if time permits.


This course will be delivered through a combination of classes and lectures totalling a minimum of 25 hours across Michaelmas Term. This year, some or all of this teaching may be delivered through a combination of virtual classes and flipped-lectures delivered as short online videos. This course includes a reading week in Week 6 of Michaelmas. Exercises will be given out to do at home during Week 6.

Formative coursework

Weekly exercises will be given.

Indicative reading

Brockwell & Davis, Time Series: Theory and Methods; Brockwell & Davis, Introduction to Time Series and Forecasting; Box & Jenkins, Time Series Analysis, Forecasting and Control; Shumway & Stoffer, Time Series Analysis and Its Applications.


Exam (80%, duration: 2 hours) in the summer exam period.
Coursework (20%) in the MT.

The course will be assessed by an examination (80%) and a coursework (20%) involving case studies which will be submitted in MT.

Student performance results

(2016/17 - 2018/19 combined)

Classification % of students
Distinction 21.4
Merit 30.5
Pass 33.6
Fail 14.5

Important information in response to COVID-19

Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Statistics

Total students 2019/20: 65

Average class size 2019/20: 22

Controlled access 2019/20: Yes

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Application of information skills
  • Application of numeracy skills