Align-TS: Structure-Guided Semantic Conditioning for Multivariate Time-Series Forecasting with Frozen Language Models

Abstract

Large language models (LLMs) have recently been used as frozen backbones for time-series forecasting, aiming to transfer general sequence priors through prompts and embedding-based interfaces. However, recent evidence suggests that many LLM-based forecasting pipelines do not reliably benefit from the language model itself, raising a fundamental question: under what design constraints does a frozen LLM provide measurable and attributable value for numerical time series prediction. Thus, we present Align-TS, a modular framework that makes the role of the frozen LLM explicit and testable by separating structure learning from semantic conditioning. First, a structure-guided encoder models inter-variable dependencies by operating attention over variable tokens, producing representations that preserve multivariate structure. Second, a languagespace recasting module converts structure-aware patches into embeddings that are compatible with the frozen LLM without requiring backbone updates. Third, an alignment-driven conditioning module produces task-aware prompt representations and aligns them with numerical embeddings to reduce entanglement and stabilize inference. This design yields a controllable pathway in which the LLM functions as a semantic conditioner rather than a monolithic predictor. Experiments on multiple public multivariate time-series forecasting benchmarks demonstrate that Align-TS achieves competitive and often superior forecasting performance across diverse domains while keeping the language model fully frozen.