Multi-Scale Hypergraph Meets LLMs: Aligning Large Language Models for Time Series Analysis
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.
Cite
Text
Shang et al. "Multi-Scale Hypergraph Meets LLMs: Aligning Large Language Models for Time Series Analysis." International Conference on Learning Representations, 2026.Markdown
[Shang et al. "Multi-Scale Hypergraph Meets LLMs: Aligning Large Language Models for Time Series Analysis." International Conference on Learning Representations, 2026.](https://mlanthology.org/iclr/2026/shang2026iclr-multiscale/)BibTeX
@inproceedings{shang2026iclr-multiscale,
title = {{Multi-Scale Hypergraph Meets LLMs: Aligning Large Language Models for Time Series Analysis}},
author = {Shang, Zongjiang and Cui, Dongliang and Wu, Binqing and Chen, Ling},
booktitle = {International Conference on Learning Representations},
year = {2026},
url = {https://mlanthology.org/iclr/2026/shang2026iclr-multiscale/}
}