STRIDE: Reasoning-Aware Training for Time Series Forecasting
Researchers have introduced STRIDE (Strategic Time-series Reasoning Injected via Distilled Embeddings), a novel framework designed to enhance Time Series Foundation Models (TSFMs) by integrating Large Language Model (LLM) reasoning capabilities. Traditional TSFMs lack qualitative reasoning, while direct LLM application to temporal data suffers from modality gaps and computational inefficiencies due to tokenization issues. STRIDE resolves this by distilling reasoning traces into a lightweight LLM and projecting its hidden states as a cross-modal prior into the numerical encoder, avoiding discrete token fragmentation. The architecture is jointly optimized using cross-entropy and quantile losses. Evaluations on GIFT-Eval and TFRBench demonstrate that STRIDE achieves state-of-the-art numerical forecasting performance, surpassing existing TSFMs in both in-domain and out-of-domain scenarios. Functioning as a plug-and-play enhancement, it consistently improves diverse models like Chronos-2 and Timer-S1. This approach equips TSFMs with human-interpretable reasoning while significantly boosting predictive accuracy, marking a significant advancement in combining semantic reasoning with continuous numerical forecasting.
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STRIDE: Reasoning-Aware Training for Time Series Forecasting
Researchers have introduced STRIDE (Strategic Time-series Reasoning Injected via Distilled Embeddings), a novel framework designed to enhance Time Series Foundation Models (TSFMs) by integrating Large Language Model (LLM) reasoning capabilities. Traditional TSFMs lack qualitative reasoning, while direct LLM application to temporal data suffers from modality gaps and computational inefficiencies due to tokenization issues. STRIDE resolves this by distilling reasoning traces into a lightweight LLM and projecting its hidden states as a cross-modal prior into the numerical encoder, avoiding discrete token fragmentation. The architecture is jointly optimized using cross-entropy and quantile losses. Evaluations on GIFT-Eval and TFRBench demonstrate that STRIDE achieves state-of-the-art numerical forecasting performance, surpassing existing TSFMs in both in-domain and out-of-domain scenarios. Functioning as a plug-and-play enhancement, it consistently improves diverse models like Chronos-2 and Timer-S1. This approach equips TSFMs with human-interpretable reasoning while significantly boosting predictive accuracy, marking a significant advancement in combining semantic reasoning with continuous numerical forecasting.
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