Nonseparable Space-Time Stationary Covariance Functions on Networks cross Time

TL;DR

This paper develops new nonseparable space-time stationary covariance functions for complex network domains and seasonal time, with applications demonstrating improved modeling of traffic accident data.

Contribution

It introduces novel covariance functions for space-time data on generalized networks and circular time, with practical construction guidance and validation through simulations and real data.

Findings

Successfully recover model parameters in simulations

Improved model performance on traffic accident data

Effective covariance specification for complex networks

Abstract

The advent of data science has provided an increasing number of challenges with high data complexity. This paper addresses the challenge of space-time data where the spatial domain is not a planar surface, a sphere, or a linear network, but a generalized network (termed a graph with Euclidean edges). Additionally, data are repeatedly measured over different temporal instants. We provide new classes of nonseparable space-time stationary covariance functions where {\em space} can be a generalized network, a Euclidean tree, or a linear network, and where time can be linear or circular (seasonal). Because the construction principles are technical, we focus on illustrations that guide the reader through the construction of statistically interpretable examples. A simulation study demonstrates that we can recover the correct model when compared to misspecified models. In addition, our simulation studies show that we effectively recover simulation parameters. In our data analysis, we consider a traffic accident dataset that shows improved model performance based on covariance specifications and network-based metrics.