arXiv:2503. 24007v4 Announce Type: replace-cross Abstract: In time series forecasting, covariates represent external factors that influence target variables.
By Yosuke Yamaguchi, Issei Suemitsu, Wenpeng Wei
arXiv:2610.20739v1 Announce Type: new
Abstract: Quantile forecasts summarize uncertainty at each future time step, while diffusion forecasters represent it through sampled future trajectories. We stu...
By Shadi Omari, Bashar Khoury, Assaf Schuster
The paper introduces TiMi, a framework that enhances time series transformers with a Multimodal Mixture-of-Experts (MMoE) module to incorporate multimodal data, especially textual information, into forecasting. TiMi leverages large language models to generate future inferences that guide predictions, eliminating the need for explicit representation alignment. Experiments show TiMi achieves state‑of‑the‑art performance on sixteen real‑world multimodal forecasting benchmarks, outperforming advanced baselines while maintaining adaptability and interpretability.
By Jiafeng Lin, Yuxuan Wang, Huakun Luo, Jianmin Wang, Zhongyi Pei
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.
By Zhente Zhang, Zhengwei Ni, Wei Fan
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...
The paper introduces a new diffusion-based approach for probabilistic weather forecasting that incorporates auxiliary conditional denoising tasks. By predicting future states from both clean and corrupted contexts, the model reduces ambiguity and learns conditional distributions across noise levels using a proper scoring rule. Experiments demonstrate improved one‑step forecasting, especially at longer horizons, and better calibration in high‑dimensional global weather scenarios.
By Joonhyeong Park, Giung Nam, Hyungi Lee, Kyunghyun Cho, Byoungwoo Park, Juho Lee