新框架通过检索和新颖的架构增强时间序列预测 · 跟踪 4 个来源

报道来源 [4]

1. arXiv cs.LG TIER_1 English(EN) · Lingyu Jiang, Lingyu Xu, Peiran Li, Dengzhe Hou, Qianwen Ge, Dingyi Zhuang, Shuo Xing, Wenjing Chen, Xiangbo Gao, Ting-Hsuan Chen, Xueying Zhan, Xin Zhang, Ziming Zhang, Zhengzhong Tu, Michael Zielewski, Kazunori Yamada, Fangzhou Lin · 2026-08-12 04:00

   TimePre：在概率时间序列预测中实现准确性、效率和稳定性

   arXiv:2511.18539v3 Announce Type: replace Abstract: We propose TimePre, a simple framework that unifies the efficiency of Multilayer Perceptron (MLP)-based models with the distributional flexibility of Multiple Choice Learning (MCL) for Probabilistic Time-Series Forecasting (PTSF…

2. arXiv cs.AI TIER_1 English(EN) · Yang Zhang, Rui Su · 2026-08-10 04:00

   KReF：用于长期时间序列预测和预测不确定性的无训练检索

   arXiv:2608.06748v1 Announce Type: cross Abstract: Probabilistic long-term time-series forecasting commonly relies on trained models. Training-free conformal methods typically construct intervals around a pre-existing point forecaster and do not natively represent a complete predi…

3. arXiv cs.AI TIER_1 English(EN) · Yixiong Xiao, Congxi Xiao, Jingbo Zhou · 2026-08-07 04:00

   TS-RAG：用于时间序列预测的检索增强生成

   arXiv:2608.06223v1 Announce Type: new Abstract: While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited. Since RA…

4. Hugging Face Daily Papers TIER_1 English(EN) · 2026-08-06 16:12

   TS-RAG：用于时间序列预测的检索增强生成

   While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited. Since RAG has proven effective in enhancing the capabili…