LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling

TL;DR

The paper introduces LLM-TS Integrator, a novel framework that combines large language models with traditional time series models using mutual information and sample reweighting to improve predictive performance across various tasks.

Contribution

It presents a new integration framework that leverages LLMs within traditional TS models through mutual information maximization and dynamic sample reweighting, enhancing modeling capabilities.

Findings

Achieves state-of-the-art performance on five TS tasks.

Effectively combines LLM insights with traditional models.

Improves predictive accuracy through mutual information maximization.

Abstract

Time series~(TS) modeling is essential in dynamic systems like weather prediction and anomaly detection. Recent studies utilize Large Language Models (LLMs) for TS modeling, leveraging their powerful pattern recognition capabilities. These methods primarily position LLMs as the predictive backbone, often omitting the mathematical modeling within traditional TS models, such as periodicity. However, disregarding the potential of LLMs also overlooks their pattern recognition capabilities. To address this gap, we introduce \textit{LLM-TS Integrator}, a novel framework that effectively integrates the capabilities of LLMs into traditional TS modeling. Central to this integration is our \textit{mutual information} module. The core of this \textit{mutual information} module is a traditional TS model enhanced with LLM-derived insights for improved predictive abilities. This enhancement is achieved by maximizing the mutual information between traditional model's TS representations and LLM's textual representation counterparts, bridging the two modalities. Moreover, we recognize that samples vary in importance for two losses: traditional prediction and mutual information maximization. To address this variability, we introduce the \textit{sample reweighting} module to improve information utilization. This module assigns dual weights to each sample: one for prediction loss and another for mutual information loss, dynamically optimizing these weights via bi-level optimization. Our method achieves state-of-the-art or comparable performance across five mainstream TS tasks, including short-term and long-term forecasting, imputation, classification, and anomaly detection.