Bayesian Spillover Graphs for Dynamic Networks. (arXiv:2203.01912v1 [stat.ME])

We present Bayesian Spillover Graphs (BSG), a novel method for learning
temporal relationships, identifying critical nodes, and quantifying uncertainty
for multi-horizon spillover effects in a dynamic system. BSG leverages both an
interpretable framework via forecast error variance decompositions (FEVD) and
comprehensive uncertainty quantification via Bayesian time series models to
contextualize temporal relationships in terms of systemic risk and prediction
variability. Forecast horizon hyperparameter $h$ allows for learning both
short-term and equilibrium state network behaviors. Experiments for identifying
source and sink …

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