To provide an advanced understanding of the core principles of time series analysis. To ensure students are competent in the use of time series methods, and are familiar with the relevant statistical software. To introduce to students an appreciation of recent developments in time series analysis, and of the links between the theory of the topics and their practical application in various industries. To develop knowledge, understanding and skills in applied computing and statistics.

Stationary processes, autoregressive and moving average processes, trend, seasonality, model building, estimation and forecasting, spectral analysis and estimation, Kalman filtering and predictions, higher-order spectral analysis, nonlinear and non-Gaussian time series, weakly and strictly stationary stochastic processes, ergodic and ensemble theory, time and frequency domain, spectral decomposition theory, multivariate spectra, estimation and inference of non-stationary time series.