SGCMA transfers an HMM state-transition prior learned from text into time series patches, then aligns patch embeddings to language tokens in each state, enabling a frozen GPT-2 to forecast as well as or better than tuned baselines.
Nhits: Neural hierarchical interpolation for time series fore- casting
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Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment
SGCMA transfers an HMM state-transition prior learned from text into time series patches, then aligns patch embeddings to language tokens in each state, enabling a frozen GPT-2 to forecast as well as or better than tuned baselines.