mlsanityData to 7 Oct 2026

Neural Temporal Point Processes: A Review

arXiv 2104.03528 ↗First posted 8 Apr 2021cs.LGcited by 113

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Abstract (arXiv)

Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences. Neural TPPs combine the fundamental ideas from point process literature with deep learning approaches, thus enabling construction of flexible and efficient models. The topic of neural TPPs has attracted significant attention in the recent years, leading to the development of numerous new architectures and applications for this class of models. In this review paper we aim to consolidate the existing body of knowledge on neural TPPs. Specifically, we focus on important design choices and general principles for defining neural TPP models. Next, we provide an overview of application areas commonly considered in the literature. We conclude this survey with the list of open challenges and important directions for future work in the field of neural TPPs.

Key facts. Cited by 113 papers in the map (data through 7 Oct 2026). Builds on 30 of its 73 references that are in the map. First posted on arXiv 8 Apr 2021.

Builds on

30 of 73 references.

References · 30

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 443
  3. 2019Monte Carlo Gradient Estimation in Machine Learningcited by 281
  4. 2020Transformer Hawkes Processcited by 230
  5. 2017Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphscited by 149
  6. 2019Neural Jump Stochastic Differential Equationscited by 147
  7. 2019Intensity-Free Learning of Temporal Point Processescited by 138
  8. 2019Fully Neural Network based Model for General Temporal Point Processescited by 119
  9. 2017Modeling The Intensity Function Of Point Process Via Recurrent Neural Networkscited by 93
  10. 2020Neural Spatio-Temporal Point Processescited by 92
  11. 2013RNADE: The real-valued neural autoregressive density-estimatorcited by 65
  12. 2017Wasserstein Learning of Deep Generative Point Process Modelscited by 46
18 more
  1. 2018Learning Temporal Point Processes via Reinforcement Learningcited by 40
  2. 2020Neural Temporal Point Processes For Modelling Electronic Health Recordscited by 39
  3. 2016Uncovering Causality from Multivariate Hawkes Integrated Cumulantscited by 38
  4. 2015COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolutioncited by 37
  5. 2020CAUSE: Learning Granger Causality from Event Sequences using Attribution Methodscited by 36
  6. 2018Deep Reinforcement Learning of Marked Temporal Point Processescited by 32
  7. 2014Discovering Latent Network Structure in Point Process Datacited by 28
  8. 2020Fast and Flexible Temporal Point Processes with Triangular Mapscited by 28
  9. 2019Uncertainty on Asynchronous Time Event Predictioncited by 27
  10. 2019Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Informationcited by 24
  11. 2020Noise-Contrastive Estimation for Multivariate Point Processescited by 20
  12. 2020User-Dependent Neural Sequence Models for Continuous-Time Event Datacited by 17
  13. 2017A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clusteringcited by 16
  14. 2017Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformationcited by 12
  15. 2018Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictionscited by 8
  16. 2018Steering Social Activity: A Stochastic Optimal Control Point Of Viewcited by 6
  17. 2019Intermittent Demand Forecasting with Deep Renewal Processescited by 6
  18. 2013Assessment of Point Process Models for Earthquake Forecastingcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 9; 2023: 25; 2024: 34; 2025: 23; 2026: 13.

113 papers.

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  1. 2022Transformers in Time Series: A Surveycited by 489
  2. 2021Neural Flows: Efficient Alternative to Neural ODEscited by 99
  3. 2021Neural Point Process for Learning Spatiotemporal Event Dynamicscited by 42
  4. 2022Exploring Generative Neural Temporal Point Processcited by 30
  5. 2023Add and Thin: Diffusion for Temporal Point Processescited by 22
  6. 2021Counterfactual Temporal Point Processescited by 20
  7. 2023Meta Temporal Point Processescited by 16
  8. 2023Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Timecited by 14
  9. 2023On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Datacited by 13
  10. 2023Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Predictioncited by 13
  11. 2021Region Invariant Normalizing Flows for Mobility Transfercited by 12
  12. 2024Unlocking Point Processes through Point Set Diffusioncited by 12
38 more
  1. 2025GeoGen: A Two-stage Coarse-to-Fine Framework for Fine-grained Synthetic Location-based Social Network Trajectory Generationcited by 12
  2. 2022ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequencescited by 11
  3. 2021An Empirical Study: Extensive Deep Temporal Point Processcited by 10
  4. 2023Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysiscited by 10
  5. 2023A Systematic Review for Transformer-based Long-term Series Forecastingcited by 10
  6. 2023Interacting Diffusion Processes for Event Sequence Forecastingcited by 9
  7. 2024Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasauruscited by 8
  8. 2024TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Modelscited by 8
  9. 2024EventFlow: Forecasting Temporal Point Processes with Flow Matchingcited by 8
  10. 2022Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processescited by 7
  11. 2023Conditional Generative Modeling for High-dimensional Marked Temporal Point Processescited by 7
  12. 2024Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processescited by 7
  13. 2024Dynamical Survival Analysis with Controlled Latent Statescited by 7
  14. 2025OpenSTARLab: Open Approach for Spatio-Temporal Agent Data Analysis in Soccercited by 7
  15. 2025Edit-Based Flow Matching for Temporal Point Processescited by 7
  16. 2021Detecting Anomalous Event Sequences with Temporal Point Processescited by 6
  17. 2024Harnessing Event Sensory Data for Error Pattern Prediction in Vehicles: A Language Model Approachcited by 6
  18. 2025A Survey of Link Prediction in Temporal Networkscited by 6
  19. 2023Modeling Events and Interactions through Temporal Processes -- A Surveycited by 5
  20. 2024Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matchingcited by 5
  21. 2024Retrieval of Temporal Event Sequences from Textual Descriptionscited by 5
  22. 2025Neural Spatiotemporal Point Processes: Trends and Challengescited by 5
  23. 2021Modelling Direct Messaging Networks with Multiple Recipients for Cyber Deceptioncited by 4
  24. 2021Dynamic Representation Learning with Temporal Point Processes for Higher-Order Interaction Forecastingcited by 4
  25. 2024TPP-Gaze: Modelling Gaze Dynamics in Space and Time with Neural Temporal Point Processescited by 4
  26. 2022Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processescited by 3
  27. 2023TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequencescited by 3
  28. 2024TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health Recordscited by 3
  29. 2024RoTHP: Rotary Position Embedding-based Transformer Hawkes Processcited by 3
  30. 2024Federated Neural Nonparametric Point Processescited by 3
  31. 2026The Taxonomies, Training, and Applications of Event Stream Modelling for Electronic Health Recordscited by 3
  32. 2022Deception for Cyber Defence: Challenges and Opportunitiescited by 2
  33. 2023Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identificationcited by 2
  34. 2023Probabilistic Modeling for Sequences of Sets in Continuous-Timecited by 2
  35. 2024Interpretable Neural Temporal Point Processes for Modelling Electronic Health Recordscited by 2
  36. 2024Cumulative Hazard Function Based Efficient Multivariate Temporal Point Process Learningcited by 2
  37. 2024On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processescited by 2
  38. 2024System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processescited by 2

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