Online Program Home
My Program

Abstract Details

Activity Number:
216867 - Bayesian Time Series Analysis and Forecasting (ADDED FEE)
Type: Professional Development
Date/Time: Monday, July 29, 2019 : 8:30 AM to 5:00 PM
Sponsor: ASA
Abstract #308042
Title: Bayesian Time Series Analysis and Forecasting (ADDED FEE)
Author(s): Marco Ferreira* and Raquel Prado*
Companies: UC Santa Cruz-Baskin School of Engineering and Virginia Tech

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 from business, finance, signal processing, environmental sciences, and the biomedical sciences.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program