RAID: Semantic Graph Diffusion for True Cold-Start and Cross-Lingual Forecasting

Abstract: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.

Artificial Intelligence (cs.AI)

arXiv:2606.16925 [cs.AI]

(or arXiv:2606.16925v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.16925 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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