Dynamic network signal processing using latent threshold models

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• 作者：Jouchi Nakajima¹ ; ^{jouchi.nakajima@boj.or.jp" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Mike West ; ^{mw@stat.duke.edu" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Dynamic graphical models ; EEG signal processing ; Financial time series ; Multivariate volatility ; Sparse time-varying regression
• 刊名：Digital Signal Processing
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：47
• 期：Complete
• 页码：5-16
• 全文大小：3929 K

文摘

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.