Handbook

The course is concerned with the theory and application of appropriate statistical techniques for analysis of time series data -- data collected over time. Through a series of lectures, the course will help students develop a sound understanding of stationary and non-stationary time series models and their properties in discrete time. The topics include the theory of stationary time series; methods for trend and seasonal analysis and adjustment; modelling and forecasting with autoregressive moving average processes; modelling the impact of exogenous or intervention variables on responses; models for stochastic volatility and long-term dependence; regression models for time series. Applications are primarily drawn from finance and public health and the R software environment is used to perform appropriate analyses for numerical examples.