Space-Time Covariance Models on Networks with An Application on Streams

TL;DR

This paper develops new space-time covariance models for processes on networks, especially Euclidean trees, filling a gap in modeling dependencies on complex network structures, and demonstrates their application on stream temperature data.

Contribution

It introduces a broad class of parametric, non-separable space-time covariance models on networks, including Euclidean trees, using various mathematical techniques and compares their predictive performance.

Findings

Models effectively capture space-time dependence on networks.

Euclidean tree models improve prediction accuracy.

Linkage between different covariance classes is established.

Abstract

The second-order, small-scale dependence structure of a stochastic process defined in the space-time domain is key to prediction (or kriging). While great efforts have been dedicated to developing models for cases in which the spatial domain is either a finite-dimensional Euclidean space or a unit sphere, counterpart developments on a generalized linear network are practically non-existent. To fill this gap, we develop a broad range of parametric, non-separable space-time covariance models on generalized linear networks and then an important subgroup -- Euclidean trees by the space embedding technique -- in concert with the generalized Gneiting class of models and 1-symmetric characteristic functions in the literature, and the scale mixture approach. We give examples from each class of models and investigate the geometric features of these covariance functions near the origin and at infinity. We also show the linkage between different classes of space-time covariance models on Euclidean trees. We illustrate the use of models constructed by different methodologies on a daily stream temperature data set and compare model predictive performance by cross validation.