mlsanityData to 14 Oct 2026

Imputing Missing Events in Continuous-Time Event Streams

arXiv 1905.05570 ↗First posted 14 May 2019cs.LG cs.AI stat.MLcited by 44

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Abstract (arXiv)

Events in the world may be caused by other, unobserved events. We consider sequences of events in continuous time. Given a probability model of complete sequences, we propose particle smoothing---a form of sequential importance sampling---to impute the missing events in an incomplete sequence. We develop a trainable family of proposal distributions based on a type of bidirectional continuous-time LSTM: Bidirectionality lets the proposals condition on future observations, not just on the past as in particle filtering. Our method can sample an ensemble of possible complete sequences (particles), from which we form a single consensus prediction that has low Bayes risk under our chosen loss metric. We experiment in multiple synthetic and real domains, using different missingness mechanisms, and modeling the complete sequences in each domain with a neural Hawkes process (Mei & Eisner 2017). On held-out incomplete sequences, our method is effective at inferring the ground-truth unobserved events, with particle smoothing consistently improving upon particle filtering.

Key facts. Cited by 44 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 May 2019.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 11; 2024: 15; 2025: 6; 2026: 5.

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  1. 2021Transformer Embeddings of Irregularly Spaced Events and Their Participantscited by 93
  2. 2023EasyTPP: Towards Open Benchmarking Temporal Point Processescited by 68
  3. 2022HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequencescited by 57
  4. 2023Anticipatory Music Transformercited by 36
  5. 2024Decomposable Transformer Point Processescited by 16
  6. 2022Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequencescited by 14
  7. 2024HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?cited by 13
  8. 2024Deep Continuous-Time State-Space Models for Marked Event Sequencescited by 11
  9. 2021Learning to Assimilate in Chaotic Dynamical Systemscited by 10
  10. 2023Interacting Diffusion Processes for Event Sequence Forecastingcited by 10
  11. 2025In-Context Learning of Temporal Point Processes with Foundation Inference Modelscited by 10
  12. 2024EventFlow: Forecasting Temporal Point Processes with Flow Matchingcited by 8
32 more
  1. 2024Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matchingcited by 6
  2. 2022Probabilistic Querying of Continuous-Time Event Sequencescited by 5
  3. 2022Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processescited by 3
  4. 2024A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passingcited by 3
  5. 2023Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identificationcited by 2
  6. 2023Probabilistic Modeling for Sequences of Sets in Continuous-Timecited by 2
  7. 2025ADiff4TPP: Asynchronous Diffusion Models for Temporal Point Processescited by 2
  8. 2023Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashingcited by 1
  9. 2023Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flowscited by 1
  10. 2024Pretext Training Algorithms for Event Sequence Datacited by 1
  11. 2025Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processescited by 1
  12. 2026ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Seriescited by 1
  13. 2021Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognitioncited by 0
  14. 2023Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inferencecited by 0
  15. 2023Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adaptercited by 0
  16. 2023GMTR: Graph Matching Transformerscited by 0
  17. 2023DarkShot: Lighting Dark Images with Low-Compute and High-Qualitycited by 0
  18. 2024TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processingcited by 0
  19. 2024TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networkscited by 0
  20. 2024On the Efficient Marginalization of Probabilistic Sequence Modelscited by 0
  21. 2024T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Imagecited by 0
  22. 2024Strategic Arms with Side Communication Prevail Over Low-Regret MAB Algorithmscited by 0
  23. 2024Towards General Deepfake Detection with Dynamic Curriculumcited by 0
  24. 2024Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphscited by 0
  25. 2024Unsupervised Event Outlier Detection in Continuous Timecited by 0
  26. 2025Mask6D: Masked Pose Priors For 6D Object Pose Estimationcited by 0
  27. 2025Unified Flow Matching for Long Horizon Event Forecastingcited by 0
  28. 2025Learning Marked Temporal Point Process Explanations based on Counterfactual and Factual Reasoningcited by 0
  29. 2026EVIL: Evolving Interpretable Algorithms for Zero-Shot Inference on Event Sequences and Time Series with LLMscited by 0
  30. 2026Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocolscited by 0
  31. 2026Latent Block-Diffusion Temporal Point Processes: A Semi-Autoregressive Framework for Asynchronous Event Sequence Generationcited by 0
  32. 2026Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspectivecited by 0