🤖 AI Summary
This work addresses the lack of reliable and reproducible comparisons among existing neural spatiotemporal point process (STPP) models, which stems from inconsistent preprocessing, coordinate normalization, data partitioning, and evaluation protocols. To remedy this, we propose SEAHORSE, a unified benchmarking framework that enables fair training, tuning, and evaluation of diverse neural STPP models through a standardized encode-evolve-decode architecture, likelihood computation in raw coordinates, and consistent evaluation protocols. We further introduce HawkesNest, a novel synthetic stress-test suite that systematically reveals the inductive biases of different models under complex event patterns. Experiments demonstrate that model performance is highly sensitive to the complexity of event dynamics: some methods degrade sharply while others remain robust, underscoring the critical value of our benchmark for analyzing model robustness.
📝 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 reproducible STPP experimentation. SEAHORSE formalizes neural STPPs through a common encode-evolve-decode interface and trains, tunes, and evaluates every model family under a single executable benchmark protocol with raw-coordinate likelihood reporting. This enables fair comparisons but, more importantly, controlled diagnostic studies. We pair SEAHORSE with HawkesNest, a synthetic stress-test suite, and show that increasing event-pattern complexity exposes each family's inductive bias, degrading some models sharply and leaving others stable. Code: https://github.com/YahyaAalaila/seahorse.