Neural Spatiotemporal Point Processes: Trends and Challenges

2025-02-13

AI Summary

This paper addresses the challenges of modeling complex dependencies and heterogeneous dynamics in real-world continuous spatiotemporal event data. It presents a systematic survey and conceptual reconstruction of neural spatiotemporal point processes (Neural STPPs). We introduce, for the first time, a unified design paradigm that structurally classifies core techniques—including RNNs, Transformers, GNNs, intensity function parameterizations, variational inference, normalizing flows, and differentiable sampling—according to data modality characteristics, thereby clarifying key modeling trade-offs. The review spans applications across transportation, ecology, social networks, and healthcare. We distill six open challenges: scalability, interpretability, few-shot generalization, uncertainty quantification, long-horizon prediction, and integration with physical constraints. Furthermore, we propose a cross-domain trend analysis framework and an open-problem taxonomy, offering a principled roadmap for both theoretical advancement and practical deployment of Neural STPPs.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Time-Series/Data Streams

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Spatiotemporal point processes (STPPs) are probabilistic models for events occurring in continuous space and time. Real-world event data often exhibit intricate dependencies and heterogeneous dynamics. By incorporating modern deep learning techniques, STPPs can model these complexities more effectively than traditional approaches. Consequently, the fusion of neural methods with STPPs has become an active and rapidly evolving research area. In this review, we categorize existing approaches, unify key design choices, and explain the challenges of working with this data modality. We further highlight emerging trends and diverse application domains. Finally, we identify open challenges and gaps in the literature.

Problem

• Modeling spatiotemporal event dependencies
• Integrating deep learning with STPPs
• Addressing challenges in heterogeneous data dynamics

Innovation

• Neural methods enhance STPPs
• Deep learning models complex dependencies
• Unified design choices reviewed