Data Augmentation for Point Process Learning

Abstract

This thesis introduces and evaluates ideas for the use of data augmentation in the area of Point Process Learning. Motivated by the regularizing effect of training with augmented data sets, we create a follow-up work to the paper ”A cross-validation-based statistical theory for point processes” by Cronie et al. [2023]. We develop methods for applying data augmentation to point process data. We discuss the possibilities of augmenting the existing process with additional data points generated by a noise process or by moving the already existing points in space. The developed methods are applied to common point process models, like the hard-core process and the area interaction process. The augmented data is then used for inference. The performed simulation study, where different discussed options are applied, shows promising results. The regularizing effect of data augmentation can be observed and thus motivates further investigation in to this topic.