TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Models
Abstract
Large language models (LLMs) have shown promise in multivariate time series classification (MTSC). To effectively adapt LLMs for MTSC, it is crucial to generate comprehensive and informative data representations. Most methods utilizing LLMs encode numerical time series into the model’s latent space, aiming to align with the semantic space of LLMs for more effective learning. Despite effectiveness, we highlight three limitations that these methods overlook: (1) they struggle to incorporate temporal and channel-specific information, both of which are essential components of multivariate time series; (2) aligning the learned representation space with the semantic space of the LLMs proves to be a significant challenge; (3) they often require task-specific retraining, preventing training-free inference despite the generalization capabilities of LLMs. To bridge these gaps, we propose TableTime, which reformulates MTSC as a table understanding task. Specifically, TableTime introduces the following strategies: (1) utilizing tabular form to unify the format of time series, facilitating the transition from the model-centric approach to the data-centric approach; (2) representing time series in text format to facilitate seamless alignment with the semantic space of LLMs; (3) designing a knowledge-task dual-driven reasoning framework, TableTime, integrating contextual information and expert-level reasoning guidance to enhance LLMs’ reasoning capabilities and enable training-free classification. Extensive experiments conducted on 10 publicly available benchmark datasets from the UEA archive validate the substantial potential of TableTime to be a new paradigm for MTSC. The code is publicly available. https://github.com/realwangjiahao/TableTime.
Jiahao Wang
Master Student
Mingyue Cheng
Associate Researcher
Qingyang Mao
Ph.D. Student
Qi Liu
Professor
