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

Muhammad Osama, Dave Zachariah, Peter Stoica

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 tuning-free 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.

Code Link: https://github.com/Muhammad-Osama/uncertainty_spatial_point_process

CMT Num: 6422