Title: Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Semantic Alignment
Primary area: learning on time series and dynamical systems
Keywords: Time Series Forecasting, Large Language Models, Semantic Alignment

Abstract: Large Language Models (LLMs) have recently demonstrated impressive performance 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 (Temporal-heterogeneity And Language-Oriented Network), 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, while maintaining higher efficiency. 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://anonymous.4open.science/r/TALON-BB00.

TL;DR: We propose TALON, an LLM-based forecasting model that bridges the modality gap between time series and language by combining temporal heterogeneity modeling with semantic alignment, achieving state-of-the-art accuracy and strong generalization.