ETM2200 Time series analysis and stochastic processes
Faculty of Business and Economics
ETM2200 Time series analysis and stochastic processes is a level 2, 6-credit-point, undergraduate unit from the Faculty of Business and Economics. It isn't offered in 2025. It needs ETM1030 and unlocks 1 unit, leading on to 2 units in all.
- Credit points
- 6
- Offered in 2025
- Not offered
- Assessment
- Exam 60%
- and 1 other task
- Workload
- 144 hours
- per semester
This is the 2025 handbook entry. See the 2027 entry.
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Requisites
Before ETM2200
Prerequisites
Pass these before you enrol.
After ETM2200
1 unit list ETM2200 as a prerequisite or corequisite.
Overview
In this unit, you will be introduced to the theoretical foundations, statistical methodologies, and practical applications of time series analysis, stochastic processes, and machine learning for forecasting. Building on the prerequisite knowledge from Probability and Statistics I, Probability and Statistics II, Risk and Survival Modelling, and Calculus I, this unit will explore the intricacies of time series analysis and stochastic processes. The curriculum places a strong emphasis on hands-on experience, allowing you to work with real-world datasets and apply statistical tools for effective analysis. This unit will equip you with the advanced skills necessary to navigate the complexities of time-dependent data, stochastic processes, and emerging machine learning techniques in the context of actuarial analytics. By fostering a deep understanding of these methodologies and providing practical exposure, this unit prepares you to tackle real-world challenges and contribute meaningfully to the field of actuarial analytics.
Upon unit completion, you will have acquired a good understanding of the theory and methodologies related to time series analysis, stochastic processes, and the use of machine learning models in forecasting, enabling you to excel in actuarial analytics, and provide sufficient expertise for use in various later units and the actuarial professional examinations.
Offerings in 2025
The 2025 handbook lists no offerings for ETM2200.
Assessment
- Within semester assessment40%
- Examination60%
Learning outcomes
When you finish this unit, you should be able to:
- 1
apply fundamental principles and concepts of Markov chains and stochastic processes for modelling
- 2
apply fundamental principles and concepts of time series for forecasting
- 3
apply fundamental principles and concepts of machine learning to solve regression and classification problems
Workload and teaching
- Tutorials24 hours
- Teaching approachProblem-based learning
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.
This unit includes problem-based learning approaches, where you engage in research, integrate theory and practice and apply knowledge and skills to develop viable solutions in response to a problem or set of problems.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
Common questions
What are the prerequisites for ETM2200?
You need ETM1030 before you enrol.
What can I take after ETM2200?
ETM2200 is a prerequisite or corequisite for 1 unit, including ETM2860. Those lead on to 2 units in all.
When is ETM2200 offered?
ETM2200 has no offerings listed in the 2025 handbook.
How much work is ETM2200?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ETM2200 have an exam?
Yes. The exam is worth 60% of the final mark, alongside 1 other task.