Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
Machine Learning
2020-07-06 v1 Machine Learning
Abstract
A spatial point process can be characterized by an intensity function which predicts the number of events that occur across space. In this paper, we develop a method to infer predictive intensity intervals by learning a spatial model using a regularized criterion. We prove that the proposed method exhibits out-of-sample prediction performance guarantees which, unlike standard estimators, are valid even when the spatial model is misspecified. The method is demonstrated using synthetic as well as real spatial data.
Cite
@article{arxiv.2007.01592,
title = {Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees},
author = {Muhammad Osama and Dave Zachariah and Petre Stoica},
journal= {arXiv preprint arXiv:2007.01592},
year = {2020}
}