Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly
Sampled Time Series
Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly
Sampled Time Series
Interacting systems are prevalent in nature. It is challenging to accurately predict the dynamics of the system if its constituent components are analyzed independently. We develop a graph-based model that unveils the systemic interactions of time series observed at irregular time points, by using a directed acyclic graph to model …