English(EN)Towards a Unified Generative Model for Scarce Time Series with Domain Experts
新框架利用大型语言模型和生成模型增强时间序列预测 · 追踪 6 个来源
作者PulseAugur 编辑部·[9 个来源]·
研究人员正在开发先进的时间序列预测框架，该框架整合了多样化的数据类型并提供可操作的见解。TokenCast 利用大型语言模型将数值序列和上下文特征转换为统一的符号表示，以实现更准确的预测。ConTex 通过学习全局策略而非实例级优化，重新构建时间序列的反事实生成，从而实现一致的实时干预。TimeMoDE 利用在多领域数据集上预训练的混合专家模型（Diffusion Transformers with Mixture-of-Experts）来解决数据稀疏性问题。另一种方法提出了一种使用条件生成对抗网络的非线性时间序列预测分布的灵活建模框架。
AI
arXiv:2606.20010v1 Announce Type: new Abstract: Current time series forecasting (TSF) research predominantly focuses on scale-homogeneous data, where different time series share similar numerical magnitude ranges. However, in real-world industrial scenarios such as financial prod…
arXiv:2606.19412v1 Announce Type: new Abstract: Time series forecasting leverages historical patterns to predict future values, but traditional methods face challenges when dealing with complex, non-stationary patterns that are difficult to memorize during training. Retrieval-aug…
Current time series forecasting (TSF) research predominantly focuses on scale-homogeneous data, where different time series share similar numerical magnitude ranges. However, in real-world industrial scenarios such as financial product sales, different time series often differ by…
arXiv:2508.09191v2 Announce Type: replace-cross Abstract: Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance. Despite recent advances, forecasting accuracy remains limite…
arXiv cs.LG
TIER_1English(EN)·Jan Voets, Hasan Tercan, Tobias Meisen, Sebastian Baum·
arXiv:2606.18049v1 Announce Type: new Abstract: Decision-making with deep learning-based time series forecasting requires not only accurate predictions but also actionable insights. However, current architectures do not inherently provide such information. Specifically, guidance …
Decision-making with deep learning-based time series forecasting requires not only accurate predictions but also actionable insights. However, current architectures do not inherently provide such information. Specifically, guidance is needed on how current conditions must be modi…
arXiv:2606.15172v1 Announce Type: new Abstract: Synthesizing realistic time series with generative models has wide-ranging applications in real-world scenarios. Despite recent progress, most existing methods are trained under the assumption of abundant training data, which substa…
arXiv stat.ML
TIER_1English(EN)·Jordi Llorens-Terrazas, Mika Meitz·
arXiv:2606.16773v1 Announce Type: cross Abstract: We propose a flexible framework for modeling the predictive distributions of nonlinear, possibly multivariate time series. Our approach expresses a general predictive distribution in an appropriate generative representation that i…
We propose a flexible framework for modeling the predictive distributions of nonlinear, possibly multivariate time series. Our approach expresses a general predictive distribution in an appropriate generative representation that is based on a folklore result from measure theoreti…
