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

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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Authors

Muhammad Osama, Dave Zachariah, Peter Stoica

Abstract

<p>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.</p>