Course overview
In this course students will learn the basics of time series analysis for data that violates the assumption of independence through temporal autocorrelation. Commencing with an introduction to temporally autocorrelated data and AutoRegressive Integrated Moving Average (ARIMA) models, content includes extensions to cope with seasonality (SARIMA), vector autoregressive models (VARMA) and conditional heteroskedasticity (ARCH) models. Students will learn the underpinning mathematical frameworks that are used to build these models, will implement the methods in R and produce written summaries of your analysis in the style of professional reports. This supports the program learning outcomes with special emphasis on higher level, technical communication of statistical reports in an unbiased way.
- Time series features
- Time series models
- State-space models
Course learning outcomes
- Evaluate temporal features of data
- Apply mathematical and statistical reasoning to time series models
- Analyse complex time series data with appropriate statistical models in R
- Produce professional statistical reports for both specialist and non-specialist audiences