Time Series Analysis and Forecasting

Postgraduate | 2026

Course page banner
area/catalogue icon
Area/Catalogue
STAT 6002
Course ID icon
Course ID
204969
Level of study
Level of study
Postgraduate
Unit value icon
Unit value
6
Course level icon
Course level
2
Study abroad and student exchange icon
Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
Yes
University-wide elective icon
University-wide elective course
Yes
Single course enrollment
Single course enrolment
Yes
alt
Note:
Course data is interim and subject to change

Course overview

This course aims to provide students with a comprehensive understanding of time series data and the statistical methods required to analyse temporal patterns. Students will explore forecasting models such as ARIMA, exponential smoothing, and machine learning techniques tailored to time series data. They will gain practical experience in building models, evaluating their accuracy, and making data-driven decisions based on temporal trends. Furthermore, students will assess the ethical implications of time series data and forecast applications, ensuring they integrate responsible data practices when developing real-world solutions.

Course learning outcomes

  • Apply fundamental time series analysis techniques to identify and model trends, seasonality, and noise within temporal data
  • Develop and evaluate ARIMA, SARIMA, and exponential smoothing models for forecasting time series data
  • Use machine learning models to forecast complex time series data and compare their performance to traditional methods
  • Interpret and assess the accuracy of time series models using appropriate evaluation metrics
  • Critically evaluate the ethical considerations in time series forecasting, ensuring responsible and unbiased applications of temporal models

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A