Automatic Integration for Spatiotemporal Neural Point Processes

TL;DR

AutoSTPP introduces a novel neural network-based method for efficient and accurate integration of complex spatiotemporal point processes, extending previous 1D approaches to 3D with improved computational efficiency.

Contribution

We extend the dual network approach to 3D spatiotemporal point processes using a decomposable parametrization with ProdNet, enabling flexible and efficient likelihood computation.

Findings

AutoSTPP effectively recovers complex intensity functions.

It outperforms existing methods on synthetic and real datasets.

It handles sharply localized intensities well.

Abstract

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.