Course overview
In this course students will learn advanced approaches to time series analysis. This will necessarily include multivariate time series and deep learning approaches to time series analysis but may also include topics such as state space models and Kalman filters, Bayesian time series, GARCH processes and Score driven models, anomaly detection methods or the integration of temporal and non-temporal data into a single model.
Course learning outcomes
- Evaluate the structure of complex time series data and recommend appropriate analysis
- Investigate complex time series data using contemporary analytical methods
- Critically review the application of time series analysis to ensure appropriate use
- Interpret time series analysis for both specialist and non-specialist audiences
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The Student Contribution amount displayed below is for students commencing a new program from 2021 onwards. If you are continuing in a program you commenced prior to 1 January 2021, or are commencing an Honours degree relating to an undergraduate degree you commenced prior to 1 January 2021, you may be charged a different Student Contribution amount from the amount displayed below. Please check the Student Contribution bands for continuing students here. If you are an international student, or a domestic student studying in a full fee paying place, and are continuing study that you commenced in 2025 or earlier, your fees will be available here before enrolments open for 2026.