mlsanityData to 7 Oct 2026

Modeling Events and Interactions through Temporal Processes -- A Survey

arXiv 2303.06067 ↗First posted 10 Mar 2023cs.LG stat.MLcited by 5

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Abstract (arXiv)

In real-world scenario, many phenomena produce a collection of events that occur in continuous time. Point Processes provide a natural mathematical framework for modeling these sequences of events. In this survey, we investigate probabilistic models for modeling event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that characterize the literature on the topic. We define an ontology to categorize the existing approaches in terms of three families: simple, marked, and spatio-temporal point processes. For each family, we systematically review the existing approaches based based on deep learning. Finally, we analyze the scenarios where the proposed techniques can be used for addressing prediction and modeling aspects.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 51 of its 118 references that are in the map. First posted on arXiv 10 Mar 2023.

Builds on

51 of 118 references.

References · 51

  1. 2020Transformer Hawkes Processcited by 230
  2. 2019Time2Vec: Learning a Vector Representation of Timecited by 208
  3. 2017Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphscited by 149
  4. 2019Intensity-Free Learning of Temporal Point Processescited by 138
  5. 2021Neural Temporal Point Processes: A Reviewcited by 113
  6. 2017Machine Learning for Survival Analysis: A Surveycited by 112
  7. 2018When will you do what? - Anticipating Temporal Occurrences of Activitiescited by 106
  8. 2017Modeling The Intensity Function Of Point Process Via Recurrent Neural Networkscited by 93
  9. 2020Neural Spatio-Temporal Point Processescited by 92
  10. 2015SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularitycited by 76
  11. 2018Deep Recurrent Survival Analysiscited by 61
  12. 2020What is More Likely to Happen Next? Video-and-Language Future Event Predictioncited by 61
38 more
  1. 2017Wasserstein Learning of Deep Generative Point Process Modelscited by 46
  2. 2018Lecture Notes: Temporal Point Processes and the Conditional Intensity Functioncited by 40
  3. 2018Learning Temporal Point Processes via Reinforcement Learningcited by 40
  4. 2016Uncovering Causality from Multivariate Hawkes Integrated Cumulantscited by 38
  5. 2019Uncertainty-Aware Anticipation of Activitiescited by 38
  6. 2015COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolutioncited by 37
  7. 2018Adversarial Time-to-Event Modelingcited by 33
  8. 2018Deep Reinforcement Learning of Marked Temporal Point Processescited by 32
  9. 2020Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Predictioncited by 31
  10. 2019A Variational Auto-Encoder Model for Stochastic Point Processescited by 30
  11. 2019Uncertainty on Asynchronous Time Event Predictioncited by 27
  12. 2019Modeling Event Propagation via Graph Biased Temporal Point Processcited by 25
  13. 2019Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Informationcited by 24
  14. 2015Learning and Forecasting Opinion Dynamics in Social Networkscited by 21
  15. 2017Topological Recurrent Neural Network for Diffusion Predictioncited by 21
  16. 2019Forecasting Future Action Sequences with Neural Memory Networkscited by 18
  17. 2021A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequencescited by 17
  18. 2017A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clusteringcited by 16
  19. 2022Counterfactual Phenotyping with Censored Time-to-Eventscited by 13
  20. 2020Event Prediction in the Big Data Era: A Systematic Surveycited by 12
  21. 2017Time-Dependent Representation for Neural Event Sequence Predictioncited by 11
  22. 2022ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequencescited by 11
  23. 2020Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Predictioncited by 10
  24. 2018Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictionscited by 8
  25. 2021Deep Structural Point Process for Learning Temporal Interaction Networkscited by 8
  26. 2021What Averages Do Not Tell -- Predicting Real Life Processes with Sequential Deep Learningcited by 7
  27. 2019Point Process Flowscited by 6
  28. 2020An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logscited by 5
  29. 2018Decoupled Learning for Factorial Marked Temporal Point Processescited by 4
  30. 2017Cheshire: An Online Algorithm for Activity Maximization in Social Networkscited by 3
  31. 2020Encoder-Decoder Generative Adversarial Nets for Suffix Generation and Remaining Time Prediction of Business Process Modelscited by 3
  32. 2022Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processescited by 3
  33. 2017Variational Policy for Guiding Point Processescited by 2
  34. 2018Time Perception Machine: Temporal Point Processes for the When, Where and What of Activity Predictioncited by 2
  35. 2019Recurrent Point Processes for Dynamic Review Modelscited by 2
  36. 2017Marked Temporal Dynamics Modeling based on Recurrent Neural Networkcited by 1
  37. 2017Universal Components of Real-world Diffusion Dynamics based on Point Processescited by 1
  38. 2021Semi-supervised Learning for Marked Temporal Point Processescited by 1

50 most cited of 51 All 51 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 1.

5 papers.

  1. 2024HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?cited by 11
  2. 2024SeqNAS: Neural Architecture Search for Event Sequence Classificationcited by 8
  3. 2024MLEM: Generative and Contrastive Learning as Distinct Modalities for Event Sequencescited by 6
  4. 2026Temporal Graph Pattern Machinecited by 2
  5. 2025Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Taskscited by 1