R-vine Models for Spatial Time Series with an Application to Daily Mean Temperature

TL;DR

This paper presents a novel spatial R-vine copula model that captures complex dependencies in spatial data, demonstrated on temperature data, reducing parameters and improving prediction over Gaussian models.

Contribution

The paper introduces a spatial R-vine copula model that incorporates spatial distances for flexible, non-Gaussian dependency modeling and efficient parameter estimation.

Findings

Reduced parameter count via distance-based parametrization

Improved prediction accuracy over Gaussian models

Effective modeling of non-Gaussian spatial dependencies

Abstract

We introduce an extension of R-vine copula models for the purpose of spatial dependency modeling and model based prediction at unobserved locations. The newly derived spatial R-vine model combines the flexibility of vine copulas with the classical geostatistical idea of modeling spatial dependencies by means of the distances between the variable locations. In particular the model is able to capture non-Gaussian spatial dependencies. For the purpose of model development and as an illustration we consider daily mean temperature data observed at 54 monitoring stations in Germany. We identify a relationship between the vine copula parameters and the station distances and exploit it in order to reduce the huge number of parameters needed to parametrize a 54-dimensional R-vine model needed to fit the data. The new distance based model parametrization results in a distinct reduction in the number of parameters and makes parameter estimation and prediction at unobserved locations feasible. The prediction capabilities are validated using adequate scoring techniques, showing a better performance of the spatial R-vine copula model compared to a Gaussian spatial model.