Time Series Analysis

Undergraduate | 2026

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Area/Catalogue
MATH 3021
Course ID icon
Course ID
200042
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.
No
University-wide elective icon
University-wide elective course
No
Single course enrollment
Single course enrolment
No
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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.  

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A