This module introduces methods for analyzing the time series data. Topics include: Stationary random processes, Autocovariance and autocorrelation functions, Discrete parameter models, Purely random processes, Autoregressive processes (first order, second order, general orders), Moving average processes, ARMA processes, General linear processes, Harmonic processes, Stochastic limiting operations and standard continuous parameter models, etc. This module is targeted at students who are interested in time series and are able to meet the prerequisite.

CILO-1: Apply advanced time series models, e.g., ARCH model, and GARCH model to deal with the real datasets. CILO-2: Demonstrate the key ideas in the proofs of the theorems on stationarity and causality of a time series. CILO-3: Conduct a complete procedure of time series analysis for a real dataset, including model selection, model estimation, and possible extensions.