Network time series models are increasingly important across many areas, in volving known or inferred underlying network structure, which can be exploited to
make sense of high–dimensional dynamic phenomena. We introduce two new associa tion measures: the network and partial network autocorrelation functions and define
Corbit (correlation–orbit) visualisation plots. Corbit plots permit interpretation of
underlying correlation structures and, crucially, aid model selection more rapidly
than general tools such as information criteria. We introduce interpretations of gen eralised network autoregressive (GNAR) processes as generalised graphical models.
We shine new light on how incorporating prior information is related to variable se lection and shrinkage in the GNAR context. We illustrate the usefulness of GNAR
models, network autocorrelations and Corbit plots for a novel network time series
modelling of COVID–19 mechanical ventilation bed occupancies at 140 NHS Trusts.
We also introduce the R–Corbit plot that shows correlations over different time pe riods or with respect to external covariates and plots that quantify the relevance of
individual nodes. Our analysis provides insight on the COVID–19 series’ underlying
dynamics, highlights two groups of geographically co–located ‘relevant’ NHS Trusts,
and demonstrates excellent predictive performance.