Mike West, Duke University
discuss some of our recent R&D with dynamic statistical models for multivariate time series
forecasting that represents a shift in modelling approaches in response to the coupled challenges
of scalability and model complexity. Building “simple” and computationally tractable models of
univariate time series is a starting point. Decouple/Recouple is an overlaid strategy for coherent
analysis: That is, “decouple” a high-dimensional system into the lowest level components for
simple/fast analysis; and then, “recouple”– on a sound theoretical basis– to rebuild the larger
multivariate process for full/formal/coherent inferences and predictions.
The approach includes dynamic dependency networks (DDNs) and the broader class of simultaneous
graphical dynamic linear models (SGDLMs) that define a framework to address these goals. Aspects of
model specification, fitting and computation include importance sampling and variational Bayes methods
to implement sequential analysis and forecasting. Studies in financial time series forecasting and portfolio
decisions highlight the utility of the models. The advances in Bayesian dynamic modelling– and in thinking
about coherent and implementable strategies for scalability to higher-dimensions (i.e. to “big, dynamic data”)–
are nicely exemplified in these contexts.
Aspects of this talk represent recent joint work with: Zoey Zhao (2013 PhD, Duke University) at Citadel llc, Chicago IL;
Lutz Gruber (2015 PhD, Technical University of Munich) at Quantco, Cambridge MA; and Meng Amy Xie (2012 BS,
Duke University) in the PhD program in Statistical Science at Duke.