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

Muhammad Osama; Dave Zachariah; Petre Stoica

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arXiv · 2007.01592

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

Muhammad Osama·Dave Zachariah·Petre Stoica

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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.

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BibTeX RIS

Muhammad Osama, Dave Zachariah, Petre Stoica. 2020-07-03. Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees. https://arxiv.org/abs/2007.01592

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