arXiv:2606.16925v1 Announce Type: new 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 ex
The proliferation of new data streams and the need for robust forecasting in 'cold-start' scenarios, particularly in emerging AI applications, drives innovation in models that don't rely solely on historical data.
This development allows AI models to make accurate predictions for new items or events without prior observations, significantly expanding the applicability and reliability of AI in dynamic environments.
Traditional time-series forecasting models are often limited by the absence of historical data for new entities; RAID addresses this by leveraging metadata and semantic graphs for 'true cold-start' predictions.
- · AI/ML researchers
- · Businesses with rapid new product introductions
- · Multilingual data platforms
- · Predictive analytics companies
- · Traditional time-series forecasting models (in cold-start scenarios)
- · Data-dependent AI systems that lack metadata integration
Improved forecasting accuracy for novel situations across various industries.
Accelerated adoption of AI in sectors previously limited by data scarcity for new entities.
Potential for new AI services and products focused on metadata-driven, cold-start predictions, reducing dependence on extensive historical datasets.
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Read at arXiv cs.AI