Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Semantic Alignment

2025-08-10

AI Summary

This paper addresses two key challenges in applying large language models (LLMs) directly to time series forecasting: (1) the intrinsic heterogeneity of temporal patterns across scales and (2) the semantic gap between continuous numerical signals and discrete linguistic representations. To this end, we propose HeterTime—a novel framework featuring (1) a Heterogeneous Temporal Encoder that captures multi-scale dynamics via a local expert mechanism, and (2) a Semantic Alignment Module that enables prompt-free, end-to-end numerical–linguistic semantic alignment for the first time. HeterTime employs segmented encoding, expert routing, and joint training. Evaluated on seven real-world benchmarks, it consistently outperforms state-of-the-art methods, achieving an average 11% reduction in MSE. Results demonstrate the effectiveness and generalizability of pattern-aware temporal modeling and prompt-free semantic alignment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Large Language Models (LLMs) have recently demonstrated impressive capabilities in natural language processing due to their strong generalization and sequence modeling capabilities. However, their direct application to time series forecasting remains challenging due to two fundamental issues: the inherent heterogeneity of temporal patterns and the modality gap between continuous numerical signals and discrete language representations. In this work, we propose TALON, a unified framework that enhances LLM-based forecasting by modeling temporal heterogeneity and enforcing semantic alignment. Specifically, we design a Heterogeneous Temporal Encoder that partitions multivariate time series into structurally coherent segments, enabling localized expert modeling across diverse temporal patterns. To bridge the modality gap, we introduce a Semantic Alignment Module that aligns temporal features with LLM-compatible representations, enabling effective integration of time series into language-based models while eliminating the need for handcrafted prompts during inference. Extensive experiments on seven real-world benchmarks demonstrate that TALON achieves superior performance across all datasets, with average MSE improvements of up to 11% over recent state-of-the-art methods. These results underscore the effectiveness of incorporating both pattern-aware and semantic-aware designs when adapting LLMs for time series forecasting. The code is available at: https://github.com/syrGitHub/TALON.

Problem

• Adapting LLMs for time series forecasting challenges
• Addressing temporal pattern heterogeneity in forecasting models
• Bridging modality gap between time series and LLMs

Innovation

• Model temporal heterogeneity via coherent segments
• Align temporal features with LLM representations
• Eliminate handcrafted prompts during inference