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Activity Details

CE_13C Sun, 7/30/2017, 8:30 AM - 5:00 PM H-Key Ballroom 11
Bayesian Time Series Analysis and Forecasting (ADDED FEE) — Professional Development Continuing Education Course
ASA , Section on Bayesian Statistical Science
This short-course covers basic principles and methods of Bayesian dynamic modeling in time series analysis and forecasting, with methodological details of central model classes explored in a range of examples. A main focus is on dynamic linear models- structure, inference, forecasting-including stationary and non-stationary time series and volatility modelling. Following detailed coverage and examples of univariate time series analysis, the course extends to linked systems of univariate series defining specific classes of multivariate models, and goes further in multivariate contexts with dynamic factor models. Aspects of simulation-based computation-forward simulation for forecasting, forward-backward simulation for analysis of state-space models, and MCMC methods for models with parameters and latent states going beyond the linear/Gaussian framework-are included. The course draws on a range of examples and from business, finance, signal processing, environmental sciences, and the biomedical sciences. Target audience: The target audience is advanced students, academics and/or professionals and practitioners with strong statistical modelling backgrounds and prior exposure to essentials of Bayesian analysis. Working facility in multivariate distribution theory, Bayesian inference and simulation-based methods of computation are prerequisites. Prior exposure to some areas of time series analysis will be useful though is not necessary.
Instructor(s): Raquel Prado, UC Santa Cruz, Mike West, Duke University, Marco Ferreira, Virginia Tech
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