Multi-Scale Hypergraph Meets LLMs: Aligning Large Language Models for Time Series Analysis

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. Recently, pre-trained foundation models, especially large language models (LLMs), have achieved great success across many fields, e.g., natural language processing (NLP) (Touvron et al., 2023; Achiam et al., 2023; Radford et al., 2021) and computer vision (CV) (Wang et al., 2024b; Pi et al., 2024) . Although the lack of large pre-training datasets and a consensus unsupervised objective makes it difficult to train foundation models for time series analysis from scratch (Sun et al., 2024; Jin et al., 2024; Pan et al., 2024) , the fundamental commonalities between natural language and time series in sequential structure and contextual dependency provide an avenue to apply LLMs for time series analysis. The core idea lies in the effective alignment of the modality between natural language and time series, either by reprogramming the input time series (Xue & Salim, 2023; Cao et al., 2024) or by introducing prompts to provide contextual information for the input time series (