Bayesian Inference for High-dimensional Time Series with a Stationary Directed Acyclic Graphical Structure

Understand

In multivariate time series analysis, understanding the underlying causal relationships among variables is often of interest for various applications.

• Directed acyclic graphs (DAGs) provide a powerful framework for representing causal dependencies.
• This paper proposes a novel Bayesian approach for modeling multivariate time series where conditional independencies and causal structure are encoded by a DAG.
• The proposed model allows structural properties such as stationarity to be easily accommodated.