Time Series Analysis

Undergraduate | 2026

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Area/Catalogue
MATH X313
Course ID icon
Course ID
207626
Level of study
Level of study
Undergraduate
Unit value icon
Unit value
6
Course level icon
Course level
3
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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
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Note:
Course data is interim and subject to change

Course overview

In this course students will learn the basics of time series analysis for data that violates the assumption of independence through temporal autocorrelation. Commencing with an introduction to temporally autocorrelated data and AutoRegressive Integrated Moving Average (ARIMA) models, content includes extensions to cope with seasonality (SARIMA), vector autoregressive models (VARMA) and conditional heteroskedasticity (ARCH) models. Students will learn the underpinning mathematical frameworks that are used to build these models, will implement the methods in R and produce written summaries of your analysis in the style of professional reports. This supports the program learning outcomes with special emphasis on higher level, technical communication of statistical reports in an unbiased way.

Course learning outcomes

  • Evaluate features of a time series using appropriate mathematical formula
  • Identify appropriate analysis techniques for data types with differing temporal features
  • Effectively analyse non-stationary, complex, time series data using R
  • Produce professional statistical reports which evaluate and summarise time series analysis for both specialist and non-specialist audiences

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A