mlsanityData to 7 Oct 2026

A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

arXiv 1708.02647 ↗First posted 8 Aug 2017stat.MEcited by 40

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Abstract (arXiv)

Self-exciting spatio-temporal point process models predict the rate of events as a function of space, time, and the previous history of events. These models naturally capture triggering and clustering behavior, and have been widely used in fields where spatio-temporal clustering of events is observed, such as earthquake modeling, infectious disease, and crime. In the past several decades, advances have been made in estimation, inference, simulation, and diagnostic tools for self-exciting point process models. In this review, I describe the basic theory, survey related estimation and inference techniques from each field, highlight several key applications, and suggest directions for future research.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Aug 2017.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 10; 2022: 2; 2023: 8; 2024: 3; 2025: 10; 2026: 6.

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  1. 2023Spatio-temporal Diffusion Point Processescited by 52
  2. 2021Neural Point Process for Learning Spatiotemporal Event Dynamicscited by 42
  3. 2021The effect of differential victim crime reporting on predictive policing systemscited by 15
  4. 2023Automatic Integration for Spatiotemporal Neural Point Processescited by 13
  5. 2021Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functionscited by 12
  6. 2021Neural Spectral Marked Point Processescited by 10
  7. 2025TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discoverycited by 9
  8. 2022Spatio-temporal point processes with deep non-stationary kernelscited by 8
  9. 2021Sequential change-point detection for mutually exciting point processes over networkscited by 7
  10. 2023Conditional Generative Modeling for High-dimensional Marked Temporal Point Processescited by 7
  11. 2023Intensity-free Convolutional Temporal Point Process: Incorporating Local and Global Event Contextscited by 6
  12. 2023TS-Diffusion: Generating Highly Complex Time Series with Diffusion Modelscited by 6
28 more
  1. 2021Interval-censored Hawkes processescited by 5
  2. 2025Neural Spatiotemporal Point Processes: Trends and Challengescited by 5
  3. 2021Understanding the Spread of COVID-19 Epidemic: A Spatio-Temporal Point Process Viewcited by 3
  4. 2021Bayesian Neural Hawkes Process for Event Uncertainty Predictioncited by 3
  5. 2023Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actionscited by 3
  6. 2024Flexible Parametric Inference for Space-Time Hawkes Processescited by 3
  7. 2021Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequencescited by 2
  8. 2024Hierarchical Probabilistic Conformal Prediction for Distributed Energy Resources Adoptioncited by 2
  9. 2025Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor Graphcited by 2
  10. 2025A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behaviorcited by 2
  11. 2022Cox-Hawkes: doubly stochastic spatiotemporal Poisson processescited by 1
  12. 2024New User Event Prediction Through the Lens of Causal Inferencecited by 1
  13. 2025Likelihood-Free Estimation for Spatiotemporal Hawkes processes with missing data and application to predictive policingcited by 1
  14. 2025Deep spatio-temporal point processes: Advances and new directionscited by 1
  15. 2026Score-Based Change-Point Detection and Region Localization for Spatio-Temporal Point Processescited by 1
  16. 2026Cascade-Aware Multi-Agent Routing: Spatio-Temporal Sidecars and Geometry-Switchingcited by 1
  17. 2019Imitation Learning of Neural Spatio-Temporal Point Processescited by 0
  18. 2021Spatio-temporal extreme event modeling of terror insurgenciescited by 0
  19. 2023Detecting Dataset Drift and Non-IID Sampling via k-Nearest Neighborscited by 0
  20. 2023Graph Representation of the Magnetic Field Topology in High-Fidelity Plasma Simulations for Machine Learning Applicationscited by 0
  21. 2025Modeling Discrete Coating Degradation Events via Hawkes Processescited by 0
  22. 2025Deep Graph Neural Point Process For Learning Temporal Interactive Networkscited by 0
  23. 2025A Semiparametric Discrete Hawkes Model with a Collapsed Gaussian-Process Priorcited by 0
  24. 2025Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Modelscited by 0
  25. 2026Multivariate Spatio-Temporal Neural Hawkes Processescited by 0
  26. 2026Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Eventscited by 0
  27. 2026Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modelingcited by 0
  28. 2026When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecastingcited by 0