arXiv:2608. 14067v1 Announce Type: new Abstract: Diffusion models offer a natural way to model uncertainty in time series forecasting, yet their iterative sampling process is often treated as a uniformly beneficial refinement procedure.
arXiv:2607. 22599v1 Announce Type: new Abstract: Diffusion models have become a widely used framework for probabilistic time series forecasting, modeling the distribution of future values given an observed history.
The paper introduces Physics‑SIMS‑TS, a conditional diffusion model designed for long‑horizon oil and gas production forecasting. It enforces monotone decline through negative guidance, decline‑curve constraints, and isotonic projection during sampling, and incorporates spatial training augmentation and an ensembled stochastic sampler to produce calibrated predictive distributions. Evaluated on over 35,000 wells across three jurisdictions, Physics‑SIMS‑TS achieves the highest accuracy among diffusion forecasters and matches transformer ensembles, with only a 0.5% increase in mean squared error for monotonicity.
arXiv:2609.38632v1 Announce Type: new Abstract: Recent probabilistic weather forecasters train stochastic predictors with the continuous ranked probability score (CRPS) to generate each ensemble memb...
arXiv:2605. 19805v2 Announce Type: replace-cross Abstract: Irregular multivariate time series impose a trade-off for long-horizon forecasting: discrete methods can distort temporal structure via re-gridding, while continuous-time models often require sequential solvers prone to drift.
Probabilistic multivariate time series (MTS) forecasting is crucial for modeling complex dynamical systems. However, existing diffusion-based methods rely on task-specific conditional paradigms that l...
arXiv:2608. 11114v1 Announce Type: cross Abstract: Probabilistic forecasting plays an essential role in risk-sensitive decision-making, particularly in long-horizon settings.
arXiv:2606. 15172v1 Announce Type: new Abstract: Synthesizing realistic time series with generative models has wide-ranging applications in real-world scenarios.
arXiv:2606. 15048v1 Announce Type: new Abstract: Diffusion models are typically trained with objectives that focus on local denoising targets at individual time steps (or adjacent pairs), which do not enforce consistency between predictions along the denoising trajectory.
arXiv:2609.21382v1 Announce Type: new Abstract: Operators of service-based systems act on forecasts of how a running execution will continue, and such a forecast is actionable only if its reliability...
arXiv:2607. 17972v1 Announce Type: new Abstract: The prevailing inference framework for diffusion models formulates generation fundamentally as a problem of numerical integration.
The paper introduces Learned End-to-End Guidance Schedules (LEEGS) for diffusion models, which train a time‑dependent guidance schedule to balance data quality and requirement satisfaction while reducing sampling steps. LEEGS minimizes the guidance function over a small set of examples using stochastic gradient descent and employs a gradient approximation to cut training time by a factor of four. Experiments on tasks such as image inpainting, noisy image inverse problems, face‑ID‑guided generation, and PDE problems show that LEEGS outperforms baselines at the same computational budget or matches constant guidance with only 10% of the steps.
DynG-Diff is a new diffusion-based framework for probabilistic multivariate time‑series forecasting that addresses the challenge of information heterogeneity across variables. It uses a two‑stage training strategy with an unconditional diffusion backbone and introduces a lightweight state‑aware policy network that dynamically adjusts guidance strength based on real‑time variable reliability. The dynamic guidance is mathematically framed as local precision, allowing the model to focus on high‑confidence variables and suppress anomalous noise, leading to competitive performance and robustness on real‑world benchmarks.