  Learning continuous-time point processes is essential to many discrete event
forecasting tasks. However, integration poses a major challenge, particularly
for spatiotemporal point processes (STPPs), as it involves calculating the
likelihood through triple integrals over space and time. Existing methods for
integrating STPP either assume a parametric form of the intensity function,
which lacks flexibility; or approximating the intensity with Monte Carlo
sampling, which introduces numerical errors. Recent work by Omi et al. [2019]
proposes a dual network approach for efficient integration of flexible
intensity function. However, their method only focuses on the 1D temporal point
process. In this paper, we introduce a novel paradigm: AutoSTPP (Automatic
Integration for Spatiotemporal Neural Point Processes) that extends the dual
network approach to 3D STPP. While previous work provides a foundation, its
direct extension overly restricts the intensity function and leads to
computational challenges. In response, we introduce a decomposable
parametrization for the integral network using ProdNet. This approach,
leveraging the product of simplified univariate graphs, effectively sidesteps
the computational complexities inherent in multivariate computational graphs.
We prove the consistency of AutoSTPP and validate it on synthetic data and
benchmark real-world datasets. AutoSTPP shows a significant advantage in
recovering complex intensity functions from irregular spatiotemporal events,
particularly when the intensity is sharply localized. Our code is open-source
at https://github.com/Rose-STL-Lab/AutoSTPP.
