arXiv:2606.16863v1 Announce Type: new Abstract: Evaluation of spatiotemporal point process (STPP) models relies heavily on opaque real-world datasets, where latent generative structure is unknown and model failures are difficult to attribute. We introduce HawkesNest, a generator-aligned benchmark for controlled spatiotemporal pattern complexity built on a multivariate Hawkes backbone. HawkesNest defines four complexity axes: space--time entanglement, background heterogeneity, cross-type interaction, and domain topology. Each axis is associated with a deterministic index computed from the laten
The increasing complexity and adoption of spatiotemporal models in AI research necessitates more robust and transparent evaluation tools for better model development.
Improved benchmarks like HawkesNest will lead to more reliable and interpretable spatiotemporal AI models, crucial for applications ranging from urban planning to scientific discovery.
The ability to systematically test and attribute failures in spatiotemporal point process models becomes more formalized and actionable.
- · AI researchers
- · STPP model developers
- · Urban planning departments
- · Environmental monitoring agencies
- · Opaque black-box AI model developers
Researchers gain a standardized tool for evaluating the complexity of spatiotemporal AI models.
This standardization accelerates the development of more accurate and robust spatiotemporal AI applications in various fields.
Improved spatiotemporal modeling contributes to more efficient resource allocation, early warning systems, and predictive analytics in complex systems.
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Read at arXiv cs.LG