Dynamic network signal processing using latent threshold models

Journal Article (Journal Article)

We discuss multivariate time series signal processing that exploits a recently introduced approach to dynamic sparsity modelling based on latent thresholding. This methodology induces time-varying patterns of zeros in state parameters that define both directed and undirected associations between individual time series, so generating statistical representations of the dynamic network relationships among the series. Following an overview of model contexts and Bayesian analysis for dynamic latent thresholding, we exemplify the approach in two studies: one of foreign currency exchange rate (FX) signal processing, and one in evaluating dynamics in multiple electroencephalography (EEG) signals. These studies exemplify the utility of dynamic latent threshold modelling in revealing interpretable, data-driven dynamics in patterns of network relationships in multivariate time series.

Full Text

Duke Authors

Cited Authors

  • Nakajima, J; West, M

Published Date

  • December 1, 2015

Published In

Volume / Issue

  • 47 /

Start / End Page

  • 5 - 16

International Standard Serial Number (ISSN)

  • 1051-2004

Digital Object Identifier (DOI)

  • 10.1016/j.dsp.2015.04.008

Citation Source

  • Scopus