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
Degree list
The following degrees include this course