arXiv:2607.01022v1 Announce Type: new Abstract: Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety. Recent neural STPPs span expressive intensity models, conditional density models, continuous-time latent dynamics, normalizing-flow spatial decoders, and score-based generative mechanisms. Yet comparison remains fragile because implementations differ in preprocessing, coordinate normalization, splits, likelihood conventions, and evaluation protocols. We present SEAHORSE, a unified framework for repr
The proliferation of various neural spatiotemporal point process models necessitates standardized benchmarking to ensure rigorous comparison and accelerate research progress.
A unified framework like SEAHORSE will improve the reliability and reproducibility of research in critical AI applications such as mobility, epidemiology, and public safety, where accurate spatiotemporal event modeling is crucial.
The fragmented and inconsistent evaluation of spatiotemporal event models will be replaced by a standardized, unified benchmarking system, enabling more objective performance comparisons and faster development cycles.
- · AI researchers
- · ML platform developers
- · Public safety AI applications
- · Epidemiology modeling
- · Fragmented research efforts
- · Inconsistent model evaluation
Researchers gain a common tool for comparing spatiotemporal models, leading to clearer performance metrics.
The improved comparability accelerates the identification of superior models and best practices for real-world applications.
More robust and reliable spatiotemporal AI models could enhance decision-making in urban planning, disaster response, and public health initiatives.
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Read at arXiv cs.LG