Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916744186036224 |
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| author | Sun, Siming Zhang, Kai Jiang, Xuejun Meng, Wenchao Yang, Qinmin |
| author_facet | Sun, Siming Zhang, Kai Jiang, Xuejun Meng, Wenchao Yang, Qinmin |
| contents | The emerging paradigm of leveraging pretrained large language models (LLMs) for time series forecasting has predominantly employed linguistic-temporal modality alignment strategies through token-level or layer-wise feature mapping. However, these approaches fundamentally neglect a critical insight: the core competency of LLMs resides not merely in processing localized token features but in their inherent capacity to model holistic sequence structures. This paper posits that effective cross-modal alignment necessitates structural consistency at the sequence level. We propose the Structure-Guided Cross-Modal Alignment (SGCMA), a framework that fully exploits and aligns the state-transition graph structures shared by time-series and linguistic data as sequential modalities, thereby endowing time series with language-like properties and delivering stronger generalization after modality alignment. SGCMA consists of two key components, namely Structure Alignment and Semantic Alignment. In Structure Alignment, a state transition matrix is learned from text data through Hidden Markov Models (HMMs), and a shallow transformer-based Maximum Entropy Markov Model (MEMM) receives the hot-start transition matrix and annotates each temporal patch into state probability, ensuring that the temporal representation sequence inherits language-like sequential dynamics. In Semantic Alignment, cross-attention is applied between temporal patches and the top-k tokens within each state, and the ultimate temporal embeddings are derived by the expected value of these embeddings using a weighted average based on state probabilities. Experiments on multiple benchmarks demonstrate that SGCMA achieves state-of-the-art performance, offering a novel approach to cross-modal alignment in time series forecasting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13175 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment Sun, Siming Zhang, Kai Jiang, Xuejun Meng, Wenchao Yang, Qinmin Artificial Intelligence The emerging paradigm of leveraging pretrained large language models (LLMs) for time series forecasting has predominantly employed linguistic-temporal modality alignment strategies through token-level or layer-wise feature mapping. However, these approaches fundamentally neglect a critical insight: the core competency of LLMs resides not merely in processing localized token features but in their inherent capacity to model holistic sequence structures. This paper posits that effective cross-modal alignment necessitates structural consistency at the sequence level. We propose the Structure-Guided Cross-Modal Alignment (SGCMA), a framework that fully exploits and aligns the state-transition graph structures shared by time-series and linguistic data as sequential modalities, thereby endowing time series with language-like properties and delivering stronger generalization after modality alignment. SGCMA consists of two key components, namely Structure Alignment and Semantic Alignment. In Structure Alignment, a state transition matrix is learned from text data through Hidden Markov Models (HMMs), and a shallow transformer-based Maximum Entropy Markov Model (MEMM) receives the hot-start transition matrix and annotates each temporal patch into state probability, ensuring that the temporal representation sequence inherits language-like sequential dynamics. In Semantic Alignment, cross-attention is applied between temporal patches and the top-k tokens within each state, and the ultimate temporal embeddings are derived by the expected value of these embeddings using a weighted average based on state probabilities. Experiments on multiple benchmarks demonstrate that SGCMA achieves state-of-the-art performance, offering a novel approach to cross-modal alignment in time series forecasting. |
| title | Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.13175 |