Course Title: Time Series Analysis

Part A: Course Overview

Course Title: Time Series Analysis

Credit Points: 12.00

Important Information:

To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption. 

Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT:

Please read the Student website for additional requirements of in-person attendance: 

Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance. 


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 1 2006,
Sem 1 2007,
Sem 1 2012,
Sem 1 2014


City Campus


171H School of Science


Sem 1 2018,
Sem 1 2019,
Sem 1 2020,
Sem 1 2021,
Sem 1 2022

Course Coordinator: Dr Haydar Demirhan

Course Coordinator Phone: +61 3 9925 2729

Course Coordinator Email:

Course Coordinator Location: 15.04.15

Course Coordinator Availability: By appointment, by email

Pre-requisite Courses and Assumed Knowledge and Capabilities

Assumed knowledge:

Knowledge of R Software and basic mathematics

Required requisites:

MATH1324 Applied Analytics

Course Description

This course aims to provide you with a working knowledge of time series analysis methods as applied in economics, engineering and the natural and social sciences. The emphasis is on methods and the analysis of time series data using the R statistical software.

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics: 

1.    Personal and professional awareness  

1.1. the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions  

1.2. the ability to reflect on experience and improve your own future practice  

1.3. the ability to apply the principles of lifelong learning to any new challenge.  


2.    Knowledge and technical competence  

2.1. an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.  


3.    Problem-solving  

3.1. the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems  

3.2. an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.  


4.    Teamwork and project management  

4.1. the ability to contribute to professional work settings through effective participation in teams and organisation of project tasks  

4.2. the ability to constructively engage with other team members and resolve conflict.  


5.    Communication  

5.1. the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.  

6.    Information literacy  

6.1. the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance. 

On completion of this course you should be able to: 

  1. Present time series in an informative way, both graphically and with summary statistics; 
  2. Develop stationary and non-stationary, and seasonal and non-seasonal time series models;
  3. Estimate model parameters and compare different models developed for the same dataset in terms of their estimation and prediction accuracy;
  4. Prepare both oral and written reports to present results of time series analyses. 

Overview of Learning Activities

Learning activities of the course include lectures and practice sessions where you will apply the methodology covered in the lectures. Face-to-face class time will be divided into two parts. In the first part, methodological aspects of time series analysis will be illustrated with facilitated demonstrations, and then, students will apply the methodology over the real datasets and discuss analysis results to foster their understanding. 

Throughout the semester, students need to bring along a fully charged laptop that is able to access to the RMIT University network for the practice sessions. Students will work in small groups or pairs in the practice sessions. 

The main focus of the course will be on stationary and non-stationary time series models for seasonal and non-seasonal time series data. The contents will be explained with examples and online demonstrations in lectures. As R software will be used for all analyses, a good knowledge of R is essential for this course. Practice sessions, assignments, and a project assignment will provide an opportunity to carry out analyses following a structured format and test your understanding of the topics covered in classes. 

Overview of Learning Resources

A prescribed textbook will be used throughout the semester.  A list of recommended textbooks for this course will also be provided on Canvas and course website as well. Lecture notes will be provided as online modules and class presentations. There will also be online apps to demonstrate key concepts of time series models.

All course materials, including lecture notes, practical exercises, datasets, and assignments will be posted on Canvas LMS and will also be available over the course website.

Library Subject Guide for Mathematics & Statistics

Overview of Assessment

Note: This course has no hurdle requirements

Assessment Tasks: 

Assessment 1: Assignment Report 1
Weighting 30%
This assessment task supports CLOs 1, 2, 3, and 4. 

Assessment 2: Assignment Report 2
Weighting 20%
This assessment task supports CLOs 1, 2, 3, and 4.  

Assessment 3: Final Project (5% Oral Presentation and 45% Written Report) 
Weighting 50%
This assessment task supports CLO 1, 2, 3, and 4.