用多尺度超图对齐语言与时间序列，提升大模型分析能力
Multi-scale hypergraph meets LLMs: Aligning large language models for time series analysis
- 构建超边机制捕捉时间序列多尺度语义
- 跨模态对齐模块在不同尺度上对齐语言与数据
- 提示混合策略增强模型对时序模式的理解
近期预训练大语言模型（LLMs）在时间序列分析中取得显著进展，核心在于有效对齐自然语言与时间序列的模态。然而，自然语言与时间序列的多尺度结构尚未被充分考虑，导致大模型能力未能充分发挥。为此，我们提出MSH-LLM，一种用于时间序列分析的多尺度超图方法。具体地，设计了超边机制以增强时间序列语义空间中的多尺度语义信息；引入跨模态对齐（CMA）模块，在不同尺度上对齐自然语言与时间序列的模态；同时，采用提示混合（MoP）机制提供上下文信息，提升大模型对时间序列多尺度时序模式的理解能力。在5个不同应用场景下的27个真实世界数据集上的实验结果表明，MSH-LLM实现了当前最优性能。
原文摘要 · Abstract (English)
Recently, there has been great success in leveraging pre-trained large language models (LLMs) for time series analysis. The core idea lies in effectively aligning the modality between natural language and time series. However, the multi-scale structures of natural language and time series have not been fully considered, resulting in insufficient utilization of LLMs capabilities. To this end, we propose MSH-LLM, a Multi-Scale Hypergraph method that aligns Large Language Models for time series analysis. Specifically, a hyperedging mechanism is designed to enhance the multi-scale semantic information of time series semantic space. Then, a cross-modality alignment (CMA) module is introduced to align the modality between natural language and time series at different scales. In addition, a mixture of prompts (MoP) mechanism is introduced to provide contextual information and enhance the ability of LLMs to understand the multi-scale temporal patterns of time series. Experimental results on 27 real-world datasets across 5 different applications demonstrate that MSH-LLM achieves the state-of-the-art results.
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