Handbook

Time series data (which are generated when measurements are taken over time) are widely used in many domains of applied science and engineering. This course provides students with skills that prepare them for working in areas such as oceanography and atmospheric science, econometrics, and signal processing applications, including analysis of radar, electro-optics, communications or information systems. 

The topics covered include the theory of time series; methods for analyzing trend and periodic variability in stationary and non-stationary time series; modelling with autocorrelation estimation; moving average processes; fitting an AR process; fitting an MA process; bivariate processes; Empirical mode decomposition and their applications.