Time Series

3 Credits · Level: Graduate, Undergraduate

Prerequisite: MATH-5151 Mathematical Statistics or equivalent; MATH-5200 is optional but highly encouraged (Note: basic familiarity with R is expected). Description: This MS level course will focus on the theory and application of methods for time series data. This course discusses the modeling and forecasting of univariate time series. Emphasis will be in the following areas: Course topics include exploratory methods, white noise and random walk models, smoothing techniques, regression methods for time series data, time series models for stationary and non-stationary data including autoregressive (AR), moving average (MA) and autoregressive moving average(ARMA) models as well as advanced models (ARCH, GARCH, etc.), and advanced topics in machine learning for time series and multivariate time series (vector autoregressive models). Applications to real-world data sets will be explored using the software package R. Examples are drawn from a variety of areas including finance, business, economics, public policy, health, environment and ecology.