EventFlow: Forecasting Temporal Point Processes with Flow Matching

• URL: http://arxiv.org/abs/2410.07430v2
• Date: Wed, 28 May 2025 12:02:08 GMT
• Title: EventFlow: Forecasting Temporal Point Processes with Flow Matching
• Authors: Gavin Kerrigan, Kai Nelson, Padhraic Smyth,
• Abstract summary: In machine learning it is common to model temporal point processes in an autoregressive fashion using a neural network.<n>We propose EventFlow, a non-autoregressive generative model for temporal point processes.
• Score: 12.976042923229466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continuous-time event sequences, in which events occur at irregular intervals, are ubiquitous across a wide range of industrial and scientific domains. The contemporary modeling paradigm is to treat such data as realizations of a temporal point process, and in machine learning it is common to model temporal point processes in an autoregressive fashion using a neural network. While autoregressive models are successful in predicting the time of a single subsequent event, their performance can degrade when forecasting longer horizons due to cascading errors and myopic predictions. We propose EventFlow, a non-autoregressive generative model for temporal point processes. The model builds on the flow matching framework in order to directly learn joint distributions over event times, side-stepping the autoregressive process. EventFlow is simple to implement and achieves a 20%-53% lower error than the nearest baseline on standard TPP benchmarks while simultaneously using fewer model calls at sampling time.

Related papers

• Unified Flow Matching for Long Horizon Event Forecasting [3.0639815065447036]
  We propose a unified flow matching framework for marked temporal point processes.<n>By learning continuous-time flows for both components, our method generates coherent long horizon event trajectories without sequential decoding.<n>We evaluate our model on six real-world benchmarks and demonstrate significant improvements over autoregressive and diffusion-based baselines in both accuracy and generation efficiency.
  arXiv  Detail & Related papers  (2025-08-06T19:42:49Z)
• Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models [2.551844666707809]
  Event-based sensors are well suited for real-time processing. Current methods either collapse events into frames or cannot scale up when processing the event data directly event-by-event.
  arXiv  Detail & Related papers  (2024-04-29T08:50:27Z)
• Non-autoregressive Sequence-to-Sequence Vision-Language Models [63.77614880533488]
  We propose a parallel decoding sequence-to-sequence vision-language model that marginalizes over multiple inference paths in the decoder. The model achieves performance on-par with its state-of-the-art autoregressive counterpart, but is faster at inference time.
  arXiv  Detail & Related papers  (2024-03-04T17:34:59Z)
• Cumulative Distribution Function based General Temporal Point Processes [49.758080415846884]
  CuFun model represents a novel approach to TPPs that revolves around the Cumulative Distribution Function (CDF) Our approach addresses several critical issues inherent in traditional TPP modeling. Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction.
  arXiv  Detail & Related papers  (2024-02-01T07:21:30Z)
• Add and Thin: Diffusion for Temporal Point Processes [24.4686728569167]
  ADD-THIN is a principled probabilistic denoising diffusion model for temporal point process (TPP) networks. It operates on entire event sequences and matches state-of-the-art TPP models in density estimation. In experiments on synthetic and real-world datasets, our model matches the state-of-the-art TPP models in density estimation and strongly outperforms them in forecasting.
  arXiv  Detail & Related papers  (2023-11-02T10:42:35Z)
• Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Prediction [8.88485011274486]
  We propose a diffusion-based non-autoregressive temporal point process model for long-term event prediction in continuous time. In order to perform diffusion processes on event sequences, we develop a bidirectional map between target event sequences and the Euclidean vector space. Experiments are conducted to prove the superiority of our proposed model over state-of-the-art methods on long-term event prediction in continuous time.
  arXiv  Detail & Related papers  (2023-11-02T06:52:44Z)
• Interacting Diffusion Processes for Event Sequence Forecasting [20.380620709345898]
  We introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-sequence prediction, allowing multi-step predictions based on historical event sequences. We demonstrate that our proposal outperforms state-of-the-art baselines for long-horizon forecasting of TPP.
  arXiv  Detail & Related papers  (2023-10-26T22:17:25Z)
• Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference [21.51753838306655]
  Temporal point processes(TPPs) are the standard method for modeling such. Existing TPP models have focused on the conditional distribution of future events instead of explicitly modeling event interactions, imposing challenges for event predictions. We propose a novel approach that leverages a Neural Inference (NRI) to learn a graph that infers interactions while simultaneously learning dynamics patterns from observational data.
  arXiv  Detail & Related papers  (2023-09-06T09:47:03Z)
• Intensity-free Convolutional Temporal Point Process: Incorporating Local and Global Event Contexts [30.534921874640585]
  We propose a novel TPP modelling approach that combines local and global contexts by integrating a continuous-time convolutional event encoder with an RNN. The presented framework is flexible and scalable to handle large datasets with long sequences and complex latent patterns. To our best knowledge, this is the first work that applies convolutional neural networks to TPP modelling.
  arXiv  Detail & Related papers  (2023-06-24T22:57:40Z)
• User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems [49.75149094527068]
  We show that diffusion models can be adapted to make predictions and provide uncertainty quantification for chaotic dynamical systems. We develop a probabilistic approximation scheme for the conditional score function which converges to the true distribution as the noise level decreases. We are able to sample conditionally on nonlinear userdefined events at inference time, and matches data statistics even when sampling from the tails of the distribution.
  arXiv  Detail & Related papers  (2023-06-13T03:42:03Z)
• CEP3: Community Event Prediction with Neural Point Process on Graph [59.434777403325604]
  We propose a novel model combining Graph Neural Networks and Marked Temporal Point Process (MTPP) Our experiments demonstrate the superior performance of our model in terms of both model accuracy and training efficiency.
  arXiv  Detail & Related papers  (2022-05-21T15:30:25Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• Variational Neural Temporal Point Process [22.396329275957996]
  A temporal point process is a process that predicts which type of events is likely to happen and when the event will occur. We introduce the inference and the generative networks, and train a distribution of latent variable to deal with property on deep neural network. We empirically demonstrate that our model can generalize the representations of various event types.
  arXiv  Detail & Related papers  (2022-02-17T13:34:30Z)
• CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [107.63407690972139]
  Conditional Score-based Diffusion models for Imputation (CSDI) is a novel time series imputation method that utilizes score-based diffusion models conditioned on observed data. CSDI improves by 40-70% over existing probabilistic imputation methods on popular performance metrics. In addition, C reduces the error by 5-20% compared to the state-of-the-art deterministic imputation methods.
  arXiv  Detail & Related papers  (2021-07-07T22:20:24Z)
• Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting [48.8617204809538]
  We propose Variational Synergetic Multi-Horizon Network (VSMHN), a novel deep conditional generative model. To learn complex correlations across heterogeneous sequences, a tailored encoder is devised to combine the advances in deep point processes models and variational recurrent neural networks. Our model can be trained effectively using variational inference and generates predictions with Monte-Carlo simulation.
  arXiv  Detail & Related papers  (2021-01-31T11:00:55Z)
• A Multi-Channel Neural Graphical Event Model with Negative Evidence [76.51278722190607]
  Event datasets are sequences of events of various types occurring irregularly over the time-line. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions.
  arXiv  Detail & Related papers  (2020-02-21T23:10:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.