Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees

TL;DR

This paper introduces a regularized method for predicting spatial point processes that provides out-of-sample guarantees, even under model misspecification, validated through synthetic and real data.

Contribution

It develops a novel regularized approach for spatial intensity prediction with theoretical out-of-sample performance guarantees.

Findings

Method achieves valid out-of-sample prediction guarantees.

Demonstrated effectiveness on synthetic data.

Validated on real spatial datasets.

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.