Thinning-Stable Point Processes as a Model for Spatial Burstiness

TL;DR

This paper introduces thinning-stable point processes as a new model for capturing the spatial burstiness of data traffic in telecommunications, offering advantages over traditional Poisson models.

Contribution

It presents the properties, inference methods, and applications of thinning-stable point processes for modeling bursty spatial data in telecommunications.

Findings

Thinning-stable processes better capture burstiness than Poisson models.

Demonstrated improved modeling accuracy in telecommunications data.

Provided inference techniques for practical application.

Abstract

In modern telecommunications, spatial burstiness of data traffic poses challenges to traditional Poisson-based models. This paper describes application of thinning-stable point processes, which provide a more appropriate framework for modeling bursty spatial data. We discuss their properties, representation, inference methods, and applications, demonstrating the advantages over classical approaches.