mlsanityData to 7 Oct 2026

Recurrent Interpolants for Probabilistic Time Series Prediction

arXiv 2409.11684 ↗First posted 18 Sep 2024cs.LG stat.MLcited by 2

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Abstract (arXiv)

Sequential models like recurrent neural networks and transformers have become standard for probabilistic multivariate time series forecasting across various domains. Despite their strengths, they struggle with capturing high-dimensional distributions and cross-feature dependencies. Recent work explores generative approaches using diffusion or flow-based models, extending to time series imputation and forecasting. However, scalability remains a challenge. This work proposes a novel method combining recurrent neural networks' efficiency with diffusion models' probabilistic modeling, based on stochastic interpolants and conditional generation with control features, offering insights for future developments in this dynamic field.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 34 of its 42 references that are in the map. First posted on arXiv 18 Sep 2024.

Builds on

34 of 42 references.

References · 34

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 2022Flow Matching for Generative Modelingcited by 5,426
  8. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  9. 2023Stochastic Interpolants: A Unifying Framework for Flows and Diffusionscited by 897
  10. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  11. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 577
  12. 2013Generating Sequences With Recurrent Neural Networkscited by 549
22 more
  1. 2022Transformers in Time Series: A Surveycited by 489
  2. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 332
  3. 2020Scalable Gradients for Stochastic Differential Equationscited by 264
  4. 2023Text-to-image Diffusion Models in Generative AI: A Surveycited by 264
  5. 2023LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generationcited by 258
  6. 2021Likelihood Training of Schr"odinger Bridge using Forward-Backward SDEs Theorycited by 216
  7. 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 195
  8. 2019Latent ODEs for Irregularly-Sampled Time Seriescited by 182
  9. 2015On Wasserstein Two Sample Testing and Related Families of Nonparametric Testscited by 165
  10. 2019High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processescited by 147
  11. 2020Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flowscited by 142
  12. 2020Neural Rough Differential Equations for Long Time Seriescited by 140
  13. 2023Stochastic interpolants with data-dependent couplingscited by 94
  14. 2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 89
  15. 2024Probabilistic Forecasting with Stochastic Interpolants and F"ollmer Processescited by 61
  16. 2019Coupling techniques for nonlinear ensemble filteringcited by 48
  17. 2019GluonTS: Probabilistic Time Series Models in Pythoncited by 45
  18. 2023Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesiscited by 29
  19. 2023Provably Convergent Schr"odinger Bridge with Applications to Probabilistic Time Series Imputationcited by 26
  20. 2024Reflected Schr"odinger Bridge for Constrained Generative Modelingcited by 12
  21. 2024Variational Schr"odinger Diffusion Modelscited by 9
  22. 2022Random Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Abilitycited by 7

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Cited by year (2026 to 7 Oct)

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

2 papers.

  1. 2025Finite-Time Analysis of Discrete-Time Stochastic Interpolantscited by 4
  2. 2026ProbRes: Volatility Learning for Probabilistic Time-Series Forecastingcited by 0

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