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

Can Chen, Gabriel Oliveira, Hossein Sharifi Noghabi, Tristan Sylvain

TL;DR

This work introduces LLM-TS Integrator, a novel framework that effectively integrates the capabilities of LLMs into traditional TS modeling, and introduces the \textit{sample reweighting} module to improve information utilization.

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.

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

TL;DR

This work introduces LLM-TS Integrator, a novel framework that effectively integrates the capabilities of LLMs into traditional TS modeling, and introduces the \textit{sample reweighting} module to improve information utilization.

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.

Paper Structure

This paper contains 50 sections, 11 equations, 13 figures, 18 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Method
  4. Mutual Information
  5. Mutual Information Estimation.
  6. Text Description for Time Series.
  7. Sample Reweighting
  8. Weighting Network.
  9. Bi-level Optimization.
  10. Experimental Results
  11. Baselines.
  12. Main Results
  13. Short- and Long- Term Forecasting
  14. Setup.
  15. Results.
  16. ...and 35 more sections

Figures (13)

  • Figure 1: Illustration of LLM-TS Integrator. Module (a) enhances the traditional TS model (TimesNet) with LLM-derived insights by mutual information maximization. Module (b) optimizes sample importance for both prediction loss and mutual information loss to improve information utilization.
  • Figure 2: Model performance across different tasks.
  • Figure 3: Model comparison in classification.
  • Figure 4: CKA by Task.
  • Figure 5: ETTh1
  • ...and 8 more figures