GPU-accelerated Bayesian learning and forecasting in simultaneous graphical dynamic linear models


We discuss Bayesian analysis of dynamic models customized to learning and prediction with increasingly high-dimensional time series. A new framework of simultaneous graphical dynamic models allows the decoupling of analyses into those of a parallel set of univariate time series dynamic models, while flexiblymodeling time-varying, cross-series dependencies and volatilities. The strategy allows for exact analysis of univariate time series models that are then coherently linked to represent the full multivariate model. Computation uses importance sampling and variational Bayes ideas, and is ideally suited to GPU-based parallelization. The analysis and its GPU-accelerated implementation is scalable with time series dimension, as we demonstrate in an analysis of a 400-dimensional financial time series.

Full Text

Duke Authors

Cited Authors

  • Gruber, L; West, M

Published Date

  • March 1, 2016

Published In

Start / End Page

  • 125 - 149

Electronic International Standard Serial Number (EISSN)

  • 1931-6690

International Standard Serial Number (ISSN)

  • 1936-0975

Digital Object Identifier (DOI)

  • 10.1214/15-BA946

Citation Source

  • Scopus