mlsanityData to 7 Oct 2026

Series-to-Series Diffusion Bridge Model

arXiv 2411.04491 ↗First posted 7 Nov 2024cs.LG cs.AIcited by 3

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Diffusion models have risen to prominence in time series forecasting, showcasing their robust capability to model complex data distributions. However, their effectiveness in deterministic predictions is often constrained by instability arising from their inherent stochasticity. In this paper, we revisit time series diffusion models and present a comprehensive framework that encompasses most existing diffusion-based methods. Building on this theoretical foundation, we propose a novel diffusion-based time series forecasting model, the Series-to-Series Diffusion Bridge Model (S2DBM), which leverages the Brownian Bridge process to reduce randomness in reverse estimations and improves accuracy by incorporating informative priors and conditions derived from historical time series data. Experimental results demonstrate that S2DBM delivers superior performance in point-to-point forecasting and competes effectively with other diffusion-based models in probabilistic forecasting.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 21 of its 29 references that are in the map. First posted on arXiv 7 Nov 2024.

Builds on

21 of 29 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024TimeBridge: Better Diffusion Prior Design with Bridge Models for Time Series Generation13 Aug
  2. 2024MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process9 Mar
  3. 2023Non-autoregressive Conditional Diffusion Models for Time Series Prediction8 Jun
  4. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation7 Jul
  5. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting28 Jan

Other references · 16

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  5. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  6. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  7. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  8. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  9. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  10. 2022BBDM: Image-to-image Translation with Brownian Bridge Diffusion Modelscited by 222
  11. 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 195
  12. 2023Denoising Diffusion Bridge Modelscited by 171
4 more
  1. 2023DINOISER: Diffused Conditional Sequence Learning by Manipulating Noisescited by 50
  2. 2023Image Restoration Through Generalized Ornstein-Uhlenbeck Bridgecited by 41
  3. 2024An Analysis of Linear Time Series Forecasting Modelscited by 40
  4. 2023Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesiscited by 29

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Diffusion Models for Time Series Forecasting: A Surveycited by 11
  2. 2025Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusioncited by 3
  3. 2025Conditionally Whitened Generative Models for Probabilistic Time Series Forecastingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024The Rise of Diffusion Models in Time-Series Forecasting
  2. 2023Diffusion Models for Time Series Applications: A Survey
  3. 2026When Denoising Hurts: Rethinking the Terminal Step of Diffusion Time Series Forecasters -- Extended Version
  4. 2026Differencing the Diffusion Trajectory toward Uncertain Components for Time Series Forecasting
  5. 2024Retrieval-Augmented Diffusion Models for Time Series Forecasting
  6. 2025SimDiff: Simpler Yet Better Diffusion Model for Time Series Point Forecasting
  7. 2026A Decomposable Forward Process in Diffusion Models for Time-Series Forecasting
  8. 2024Auto-Regressive Moving Diffusion Models for Time Series Forecasting