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.

翻译：摘要：时间序列基础模型在给定非空历史窗口时展现出强大的迁移性能。然而，真正的冷启动场景——即新项目缺乏历史观测数据——违背了这一假设。我们提出RAID（检索增强迭代扩散）框架，该框架利用元数据驱动的语义检索和图条件扩散替代基于历史的相关性学习。RAID通过冻结的多语言嵌入模型将文本元数据映射到共享语义空间，并构建一个可自然扩展至未见项目的归纳检索图。它首先通过聚合语义相关邻居的信息形成基础预测，随后利用门控扩散模块优化该预测以建模残余不确定性。在严格的真实冷启动协议下，RAID在预测精度和预测区间覆盖率上均优于强大的基础模型及竞争基线，同时通过非自回归解码将推理延迟降低一个数量级。共享语义空间还实现了零样本跨语言迁移，使基于英文描述训练的模型能泛化至其他语言描述的项目，无需直接监督。