Course Title: Time Series Analysis
Part A: Course Overview
Course Title: Time Series Analysis
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
MATH1318 |
City Campus |
Postgraduate |
145H Mathematical & Geospatial Sciences |
Face-to-Face |
Sem 1 2006, Sem 1 2007, Sem 1 2012, Sem 1 2014 |
MATH1318 |
City Campus |
Postgraduate |
171H School of Science |
Face-to-Face |
Sem 1 2018, Sem 1 2019, Sem 1 2020, Sem 1 2021, Sem 1 2022, Sem 1 2023, Sem 1 2024 |
Course Coordinator: Dr Haydar Demirhan
Course Coordinator Phone: +61 3 9925 2729
Course Coordinator Email: haydar.demirhan@rmit.edu.au
Course Coordinator Location: 15.04.15
Course Coordinator Availability: By appointment, by email
Pre-requisite Courses and Assumed Knowledge and Capabilities
Required Prior Study
You should have satisfactorily completed following course/s before you commence this course.
Alternatively, you may be able to demonstrate the required skills and knowledge before you start this course.
Contact your course coordinator if you think you may be eligible for recognition of prior learning.
Assumed Knowledge
Knowledge of R Software and basic mathematics
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:
- Present time series using informative graphical representations and concise summary statistics;
- Construct time series models, distinguishing between stationary and non-stationary data , as well as recognising seasonal and non-seasonal patterns ;
- Accurately estimate model parameters and compare different models developed for the same dataset in terms of their estimation and prediction accuracy;
- Prepare comprehensive oral and written reports to communicate the outcomes of time series analyses persuasively.
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
RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course.
There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.
Overview of Assessment
Assessment Tasks
Assessment Task 1: Assignment Report 1
Weighting 30%
This assessment task supports CLOs 1, 2, 3 & 4
Assessment Task 2: Assignment Report 2
Weighting 20%
This assessment task supports CLOs 1, 2, 3 & 4
Assessment Task 3: Final Project (5% Oral Presentation and 45% Written Report)
Weighting 50%
This assessment task supports CLOs 1, 2, 3 & 4
If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.