ARMA approximation of a Non-separable Spatio-Temporal Model with Fractional Smoothnesses in Space and Time
S. Knutsen Furset, Geir-Arne Fuglstad, Espen R. Jakobsen

TL;DR
This paper introduces a new discretization method for a non-separable spatio-temporal Matérn covariance model with fractional smoothness, enabling flexible modeling and accurate parameter estimation.
Contribution
It proposes a rational approximation-based discretization that handles arbitrary smoothnesses, resulting in a VARMA process with proven convergence and practical effectiveness.
Findings
The covariance approximation converges pointwise with explicit rates.
Numerical verification shows small errors with low-order VARMA.
Correctly modeling temporal smoothness improves forecasting accuracy.
Abstract
The Mat\'ern covariance model is ubiquitous in spatial modelling, but there is no default choice for spatio-temporal modelling. In this paper, we consider the recently proposed ``diffusion-based'' extension of the spatial Mat\'ern covariance model to a spatio-temporal non-separable covariance model that allows fractional smoothnesses in space and in time. The model is described in terms of a space-time fractional stochastic partial differential equation, but currently proposed computational approaches have strong restrictions on the possible smoothnesses in time. We propose a discretization method based on rational approximations in time to handle arbitrary smoothnesses, which leads to a vector autoregressive moving average process (VARMA). We prove that the covariance function of the approximation converges pointwise, determine explicit convergence rates as a function of spatial and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
