STAT 391 Special Topics in Statistics and Biostatistics
Time series arise in many real-world applications, including economics, biology, physics, social sciences, and other related areas. In this applied course, students will learn the fundamental principles of modern time series analysis, including modeling of times series data and methods for statistical inference. Topics include univariate time series, stationary and non-stationary processes, time series regression, autoregressive integrated moving average (ARIMA) models, (generalized) autoregressive conditionally heteroscedastic (ARCH/GARCH) models, state-space models, and forecasting methods. We will emphasize applications to a variety of real data, through extensive use of the R/RStudio statistical software.
Corequisite
Required MATH-229 or STAT-229