STAT30010 Time Series

Academic Year 2024/2025

This course provides an opportunity for students to learn some basic techniques in Time Series Analysis. A Time Series is a set of measurements taken at regular intervals over a period of time. Time series pervade the worlds of economics and finance e.g. equity prices, (CPI) inflation, GNP, GDP, derivative prices, oil prices, etc. Although the techniques of Time Series Analysis are used by many people, from meteorologists to astronomers, this course will mainly focus on examples in the world of economics and finance. This course will show how to model such data and how using those models forecasts and predictions can be made.Time series analysis is not a new subject, however there have been many advances in recent years and the 2003 Nobel prize in Economics was awarded to Engle and Granger for their work in time-series econometrics. This course will cover both traditional methods and more modern approaches to Time Series Analysis. Upon completion of this course students should have mastered techniques that are extremely valuable for careers in the analysis of economic and financial data.

Topics covered include, among others:

- Introduction and basic concepts
- Autoregressive Moving Average (ARMA) models: definition and estimation of parameters
- Forecasting
- Autoregressive Integrated Moving Average (ARIMA) models: Identification and diagnostic checking
- Multivariate time series
- Cointegration

If time permits, we will also cover

- Generalised AutoRegressive Conditional Heteroskedasticity (GARCH) models
- Count time series modelling

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Curricular information is subject to change

Learning Outcomes:

Upon completion of this module students will be able to:
1. Identify the stationarity properties of a time series,
2. Model the time series using Box-Jenkins ARIMA techniques,
3. Estimate parameters for ARIMA models using a variety of procedures,
4. Produce forecasts for a given time series,
5. Be familiar with additional topics such as cointegration, vector auto regressive models...

Student Effort Hours: 
Student Effort Type Hours




Computer Aided Lab


Specified Learning Activities


Autonomous Student Learning




Approaches to Teaching and Learning:
Lectures, tutorials and computer labs. 
Requirements, Exclusions and Recommendations
Learning Requirements:

Familiarity with basic probability concepts such as Probability distribution, Expectation, Variance, Covariance and Correlation. Knowledge of the main probability distributions (normal distribution, chi-square, ...). Basic linear algebra (vectors, matrices).

Learning Recommendations:

Students should have a knowledge of statistical inference at a level equivalent to that which would be achieved upon completion of "Inferential Statistics" STAT20100.

Basic knowledge in linear algebra (vectors, matrices). Knowledge of linear models and least square estimation which would be achieved upon completion of "Data Modelling for Science" STAT20070 or "Predictive Analytics I" STAT30240 would be beneficial.

Module Requisites and Incompatibles
ECON30540 - Advd Econometrics: Time Series, STAT40700 - Time Series Analysis - Act App, STAT40860 - Time Series (online)

Additional Information:
Students must have confidence and experience in the theory and use of expected values and covariances of random variables. Lack of proficiency in this area is the primary cause of difficulty in this module.

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment(Including Essay): Assignment 2: theoretical and computational questions. n/a Alternative linear conversion grade scale 40% No


Exam (In-person): Final Exam n/a Alternative linear conversion grade scale 40% No


Assignment(Including Essay): Assignment 1: theoretical and computational questions. n/a Alternative linear conversion grade scale 40% No


Carry forward of passed components
Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

R.H. Shumway & D.S. Stoffer, Time Series Analysis and Its Applications: With R Examples, Springer, 2006 (2nd edition).
J.D. Cryer & K-S. Chan, Time Series Analysis: With Applications in R, Springer, 2008 (2nd edition).
P.J. Brockwell & R.A. Davis, Time Series: Theory and Methods, Springer, 2006 (2nd edition).
B. Pfaff, Analysis of Integrated and Cointegrated Time Series with R, 2008 (2nd edition).
Name Role
Kseniia Maksimova Tutor
Pedro Menezes De Araújo Tutor