这篇提出了RAID，用元数据语义和图扩散搞定时间序列冷启动预测，精度高、跨语言、延迟还低，挺实用的。
RAID框架利用多语言嵌入模型将文本元数据映射到共享语义空间，构建归纳检索图实现新项扩展。在严格冷启动协议下，RAID在预测准确率上超越当前强基础模型，同时预测区间覆盖率更优。其非自回归解码将推理延迟降低一个数量级。此外，RAID支持零样本跨语言迁移，仅使用英文描述训练即可泛化至其他语言。
RAID: Semantic Graph Diffusion for True Cold-Start and Cross-Lingual Forecasting
Time-series foundation models show strong transfer performance when given a non-empty history window. However, true cold-start scenarios, where a new item has no prior observations, violate this assumption. We propose RAID (Retrieval-Augmented Iterative Diffusion) a framework, which replaces history-based correlation learning with metadata-driven semantic retrieval and graph-conditioned diffusion. RAID maps textual metadata into a shared semantic space using a frozen multilingual embedding model and constructs an inductive retrieval graph that extends naturally to unseen items. It first forms a base forecast by aggregating information from semantically related neighbors, then refines this forecast with a gated diffusion module to model residual uncertainty. Under a strict true cold-start protocol, RAID outperforms strong foundation models and competitive baselines on both forecasting accuracy and prediction interval coverage, while reducing inference latency by an order of magnitude through non-autoregressive decoding. The shared semantic space also enables zero-shot cross-lingual transfer, allowing a model trained on English descriptions to generalize to items described in other languages without direct supervision.