Multi-scale hypergraph meets LLMs: Aligning large language models for time series analysis
This work improves time series analysis by better utilizing LLMs, though it appears incremental as it builds on existing alignment methods.
The paper tackled the problem of aligning large language models (LLMs) with time series data by addressing multi-scale structures, proposing MSH-LLM, which achieved state-of-the-art results on 27 real-world datasets across 5 applications.
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.