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Neural Spatio-Temporal Point Processes

arXiv 2011.04583 ↗First posted 9 Nov 2020cs.LGcited by 92

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Abstract (arXiv)

We propose a new class of parameterizations for spatio-temporal point processes which leverage Neural ODEs as a computational method and enable flexible, high-fidelity models of discrete events that are localized in continuous time and space. Central to our approach is a combination of continuous-time neural networks with two novel neural architectures, i.e., Jump and Attentive Continuous-time Normalizing Flows. This approach allows us to learn complex distributions for both the spatial and temporal domain and to condition non-trivially on the observed event history. We validate our models on data sets from a wide variety of contexts such as seismology, epidemiology, urban mobility, and neuroscience.

Key facts. Cited by 92 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 14; 2023: 20; 2024: 17; 2025: 13; 2026: 19.

92 papers.

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  1. 2021Neural Temporal Point Processes: A Reviewcited by 113
  2. 2021Neural Flows: Efficient Alternative to Neural ODEscited by 99
  3. 2023EasyTPP: Towards Open Benchmarking Temporal Point Processescited by 63
  4. 2024ContiFormer: Continuous-Time Transformer for Irregular Time Series Modelingcited by 61
  5. 2024Probabilistic Forecasting with Stochastic Interpolants and F"ollmer Processescited by 61
  6. 2023Spatio-temporal Diffusion Point Processescited by 52
  7. 2021Neural Controlled Differential Equations for Online Prediction Taskscited by 46
  8. 2021Neural Point Process for Learning Spatiotemporal Event Dynamicscited by 42
  9. 2022Exploring Generative Neural Temporal Point Processcited by 30
  10. 2022Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processescited by 23
  11. 2023Add and Thin: Diffusion for Temporal Point Processescited by 22
  12. 2022Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Mediacited by 20
38 more
  1. 2021Input Convex Gradient Networkscited by 17
  2. 2022Statistical Deep Learning for Spatial and Spatio-Temporal Datacited by 15
  3. 2022Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphscited by 13
  4. 2023Automatic Integration for Spatiotemporal Neural Point Processescited by 13
  5. 2023Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Predictioncited by 13
  6. 2024Unlocking Point Processes through Point Set Diffusioncited by 12
  7. 2023Neural Markov Jump Processescited by 11
  8. 2024Latent Logic Tree Extraction for Event Sequence Explanation from LLMscited by 9
  9. 2022Spatio-temporal point processes with deep non-stationary kernelscited by 8
  10. 2023Towards Complex Dynamic Physics System Simulation with Graph Neural ODEscited by 8
  11. 2023Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processescited by 8
  12. 2024Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasauruscited by 8
  13. 2024TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Modelscited by 8
  14. 2022Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processescited by 7
  15. 2023Conditional Generative Modeling for High-dimensional Marked Temporal Point Processescited by 7
  16. 2024Dynamical Survival Analysis with Controlled Latent Statescited by 7
  17. 2025DanmakuTPPBench: A Multi-modal Benchmark for Temporal Point Process Modeling and Understandingcited by 7
  18. 2025Edit-Based Flow Matching for Temporal Point Processescited by 7
  19. 2021Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitionscited by 6
  20. 2023TS-Diffusion: Generating Highly Complex Time Series with Diffusion Modelscited by 6
  21. 2024Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equationscited by 6
  22. 2023Modeling Events and Interactions through Temporal Processes -- A Surveycited by 5
  23. 2023Lagrangian Flow Networks for Conservation Lawscited by 5
  24. 2024DeNOTS: Stable Deep Neural ODEs for Time Seriescited by 5
  25. 2025Neural Spatiotemporal Point Processes: Trends and Challengescited by 5
  26. 2022Continuous-time stochastic gradient descent for optimizing over the stationary distribution of stochastic differential equationscited by 4
  27. 2021Bayesian Neural Hawkes Process for Event Uncertainty Predictioncited by 3
  28. 2023Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actionscited by 3
  29. 2023TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequencescited by 3
  30. 2024Learning Spatiotemporal Dynamical Systems from Point Process Observationscited by 2
  31. 2024EarthquakeNPP: A Benchmark for Earthquake Forecasting with Neural Point Processescited by 2
  32. 2024Semi-Implicit Neural Ordinary Differential Equationscited by 2
  33. 2025Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor Graphcited by 2
  34. 2025Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approachescited by 2
  35. 2025Generative Unordered Flow for Set-Structured Data Generationcited by 2
  36. 2025Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Modelcited by 2
  37. 2025Simulation-Free Differential Dynamics through Neural Conservation Lawscited by 2
  38. 2021Characteristic Neural Ordinary Differential Equationscited by 1

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