Semantic-Enhanced Time-Series Forecasting via Large Language Models

2025-08-11

AI Summary

Existing time-series forecasting methods leveraging large language models (LLMs) face two fundamental bottlenecks: (1) an inherent modality gap between linguistic knowledge structures and temporal patterns, limiting semantic representation capability; and (2) the difficulty of reconciling Transformer-based long-range modeling strengths with short-term anomaly detection. This paper proposes a semantic-enhanced LLM framework that bridges the modality gap and jointly models long- and short-term dependencies via three novel components: periodicity-aware embedding, anomaly-sensitive tokenization, and lightweight dimensionality reduction. Crucially, it adopts a frozen LLM backbone augmented with an embedded self-attention mechanism—preserving computational efficiency while enhancing token-level semantic interpretability. Extensive experiments on multiple benchmark datasets demonstrate significant improvements over state-of-the-art methods, validating both the effectiveness and efficiency of the proposed approach.

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: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Time series forecasting plays a significant role in finance, energy, meteorology, and IoT applications. Recent studies have leveraged the generalization capabilities of large language models (LLMs) to adapt to time series forecasting, achieving promising performance. However, existing studies focus on token-level modal alignment, instead of bridging the intrinsic modality gap between linguistic knowledge structures and time series data patterns, greatly limiting the semantic representation. To address this issue, we propose a novel Semantic-Enhanced LLM (SE-LLM) that explores the inherent periodicity and anomalous characteristics of time series to embed into the semantic space to enhance the token embedding. This process enhances the interpretability of tokens for LLMs, thereby activating the potential of LLMs for temporal sequence analysis. Moreover, existing Transformer-based LLMs excel at capturing long-range dependencies but are weak at modeling short-term anomalies in time-series data. Hence, we propose a plugin module embedded within self-attention that models long-term and short-term dependencies to effectively adapt LLMs to time-series analysis. Our approach freezes the LLM and reduces the sequence dimensionality of tokens, greatly reducing computational consumption. Experiments demonstrate the superiority performance of our SE-LLM against the state-of-the-art (SOTA) methods.

Problem

• Bridging modality gap between linguistic knowledge and time series patterns
• Enhancing semantic representation of time series in LLM token embedding
• Improving short-term anomaly modeling while maintaining long-range dependencies

Innovation

• Semantic-Enhanced LLM embeds periodicity into semantic space
• Plugin module models long-term and short-term dependencies
• Frozen LLM with reduced dimensionality lowers computational consumption