Multi-Scale Hypergraph Meets LLMs: Aligning Large Language Models for Time Series Analysis
Zongjiang Shang, Dongliang Cui, Binqing Wu, Ling Chen
Abstract
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. Code is available at: https://anonymous.4open.science/r/MSH-LLM-1E9B.
BibTeX
@inproceedings{
shang2026multiscale,
title={Multi-Scale Hypergraph Meets {LLM}s: Aligning Large Language Models for Time Series Analysis},
author={Zongjiang Shang and Dongliang Cui and Binqing Wu and Ling Chen},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=SbBX2dCw3y}
}