ILRST 7500

Advanced Time Series Analysis

Course Description

Course information provided by the Courses of Study 2016-2017.

Topics covered will include linear and non-linear, univariate and multivariate time series. Depending on interest, additional topics may include spatio-temporal modeling, seasonal modeling, spectral analysis, discrete-valued time series, factor models, high-dimensional time series, functional time series, network time series, non-stationary time series, change point analysis, and Bayesian and machine learning methods for time series. Many applications may be considered, from finance and economics, to neuroscience and biophysics, to urban informatics and emergency medical services.

When Offered Spring.

Permission Note Enrollment limited to: graduate students.
Prerequisites/Corequisites Prerequisite: Linear Models and 1 year graduate sequences in Mathematical Statistics, Probability, and Econometrics, or instructor permission.

View Enrollment Information