Foresail: LLM Sensor Knowledge Empowered Status-guided Network for Multivariate Time-series Classification

摘要

Multivariate time-series (MTS) classification tasks play a key role in data-driven applications spanning healthcare, finance, and mobile communication. As MTS data are typically collected from multiple interdependent sensors, the resulting temporal patterns inherently reflect the characteristics of the underlying sensing systems. Despite this connection, conventional MTS classification models predominantly focus on raw time-series data while disregarding valuable sensor-specific prior knowledge, which fundamentally constrains their classification accuracy. The emergence of large language models (LLMs) has encoded extensive sensor-related knowledge within their parameter spaces. However, effectively harnessing such knowledge to enhance MTS classification networks remains an open challenge. To address this, we propose Foresail, a status-guided neural framework that bridges this gap through systematic integration of LLM-derived sensor knowledge via the status relationship matrix and fine-grained status labels. Foresail can be seamlessly integrated with existing MTS networks to optimize performance and generate interpretable intermediate results. Experiments on irregularly and regularly sampled MTS data demonstrate that Foresail outperforms state-of-the-art approaches, achieving a notable improvement in F1-score of up to 10.9% compared to the basic MTS network.