STRIDE: Reasoning-Aware Training for Time Series Forecasting

STRIDE (Strategic Time-series Reasoning Injected via Distilled Embeddings) is a framework that embeds LLM reasoning directly into the continuous embedding space of Time Series Foundation Models (TSFMs). By distilling reasoning traces into a lightweight LLM and projecting its pooled hidden states as a continuous cross‑modal prior, STRIDE integrates semantic reasoning without fragmenting numerical values or inflating sequence lengths. The method is jointly optimized with cross‑entropy and quantile losses, can be attached to various TSFM architectures, and achieves top performance on the GIFT‑Eval leaderboard and strong results on TFRBench, with ablations showing that the gains come from strategic reasoning integration.

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• llms
• rag
• multimodal
• benchmarks