# Spatio-temporal Ornstein-Uhlenbeck processes: theory, simulation and statistical inference

arXiv: https://arxiv.org/abs/1504.08327 · HTML: https://mlsanity.com/paper/1504.08327/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2015.

## Built on by (most cited first, 3 of 3)

- [Mixed moving average field guided learning for spatio-temporal data](https://mlsanity.com/paper/2301.00736/) (2023)
- [Likelihood-based inference and forecasting for trawl processes: a stochastic optimization approach](https://mlsanity.com/paper/2308.16092/) (2023)
- [Spatio-temporal probabilistic forecast using MMAF-guided learning](https://mlsanity.com/paper/2603.15055/) (2026)

## Abstract (arXiv)

Spatio-temporal modelling is an increasingly popular topic in Statistics. Our paper contributes to this line of research by developing the theory, simulation and inference for a spatio-temporal Ornstein-Uhlenbeck process. We conduct detailed simulation studies and demonstrate the practical relevance of these processes in an empirical study of radiation anomaly data. Finally, we describe how predictions can be carried out in the Gaussian setting.