STELLA: Guiding Large Language Models for Time Series Forecasting with Semantic Abstractions
This addresses the challenge of enhancing LLM performance for time series forecasting, which is incremental as it builds on existing adaptations by introducing a novel prompting strategy.
The paper tackles the problem of underutilizing LLM reasoning in time series forecasting by proposing STELLA, a framework that mines and injects structured semantic information, resulting in outperforming state-of-the-art methods on eight benchmark datasets in long- and short-term forecasting with superior generalization in zero-shot and few-shot settings.
Recent adaptations of Large Language Models (LLMs) for time series forecasting often fail to effectively enhance information for raw series, leaving LLM reasoning capabilities underutilized. Existing prompting strategies rely on static correlations rather than generative interpretations of dynamic behavior, lacking critical global and instance-specific context. To address this, we propose STELLA (Semantic-Temporal Alignment with Language Abstractions), a framework that systematically mines and injects structured supplementary and complementary information. STELLA employs a dynamic semantic abstraction mechanism that decouples input series into trend, seasonality, and residual components. It then translates intrinsic behavioral features of these components into Hierarchical Semantic Anchors: a Corpus-level Semantic Prior (CSP) for global context and a Fine-grained Behavioral Prompt (FBP) for instance-level patterns. Using these anchors as prefix-prompts, STELLA guides the LLM to model intrinsic dynamics. Experiments on eight benchmark datasets demonstrate that STELLA outperforms state-of-the-art methods in long- and short-term forecasting, showing superior generalization in zero-shot and few-shot settings. Ablation studies further validate the effectiveness of our dynamically generated semantic anchors.