arXiv AI
GARDiff: Graph-Aligned Residual Diffusion for Probabilistic Multivariate Time-Series Forecasting
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Differencing the Diffusion Trajectory toward Uncertain Components for Time Series Forecasting
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.
Hugging Face Trending Papers
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DynG-Diff: A State-Aware Dynamic Guidance Diffusion Framework for Probabilistic Time Series Forecasting
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 Machine Learning
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DynG-Diff: A State-Aware Dynamic Guidance Diffusion Framework for Probabilistic Time Series Forecasting
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.
arXiv AI
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Multivariate Time Series Forecasting needs Cross Variable Loss
arXiv:2608. 05742v1 Announce Type: cross Abstract: Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics.
arXiv AI
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SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models
The paper introduces SGA, a method for quantifying uncertainty in multi‑step forecasts from Time Series Foundation Models (TSFMs). SGA models all possible forecast branches as a directed acyclic graph, using the graph’s complexity—derived from topology and TSFM stochasticity—to bound and measure uncertainty. Experiments on 11 TSFMs across 27 datasets show that SGA outperforms existing uncertainty‑quantification methods, offers broader sampling coverage, and reveals that larger TSFMs tend to produce lower uncertainty estimates.
arXiv Machine Learning
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When Denoising Hurts: Rethinking the Terminal Step of Diffusion Time Series Forecasters -- Extended Version
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.