MATH 351 - Time Series Data Analysis

An in-depth study of discrete time series and its applications to a wide range of areas such as finance, data sciences, engineering and biological research. The course will cover many of the fundamental time series such as white noise, AR, MA, ARMA, ARIMA and seasonal time series. It will also introduce statistical inference particularly on time series such as stationarity tests, trend estimation and period tests. The course provides preliminary results on advanced mathematical analysis for stationary time series such as spectral analysis, fast Fourier transformation and representation of stationary time series using polynomials. Finally, some more recently developed topics in time series are introduced, such as fractional processes and long memory processes. The course is designed for those who wish to apply stochastic processes to model real world objects. Prerequisite: A course in probability and at least concurrent enrollment in statistics.
Units: 4
Course Type: Seminar