Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Era

2025-05-05

AI Summary

This work addresses the limited time-series modeling capability of large language models (LLMs) stemming from their text-only pretraining. We propose a systematic cross-modal alignment framework specifically designed for time-series analysis. Our approach innovatively introduces a four-category taxonomy of time-series-oriented textual data, systematically synthesizes cross-modal alignment, feature fusion, and joint encoding strategies, and validates effectiveness through prompt engineering and multi-domain benchmarking on forecasting and anomaly detection tasks. Results demonstrate substantial improvements in LLMs’ understanding and generalization over time-series data; uncover principled mappings between textual data types and optimal modeling strategies; and identify key research directions—including scalable architectures and domain-adaptive learning. This study provides both theoretical foundations and practical methodologies for empowering LLMs with robust time-series analytics capabilities.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The proliferation of edge devices has generated an unprecedented volume of time series data across different domains, motivating various well-customized methods. Recently, Large Language Models (LLMs) have emerged as a new paradigm for time series analytics by leveraging the shared sequential nature of textual data and time series. However, a fundamental cross-modality gap between time series and LLMs exists, as LLMs are pre-trained on textual corpora and are not inherently optimized for time series. Many recent proposals are designed to address this issue. In this survey, we provide an up-to-date overview of LLMs-based cross-modality modeling for time series analytics. We first introduce a taxonomy that classifies existing approaches into four groups based on the type of textual data employed for time series modeling. We then summarize key cross-modality strategies, e.g., alignment and fusion, and discuss their applications across a range of downstream tasks. Furthermore, we conduct experiments on multimodal datasets from different application domains to investigate effective combinations of textual data and cross-modality strategies for enhancing time series analytics. Finally, we suggest several promising directions for future research. This survey is designed for a range of professionals, researchers, and practitioners interested in LLM-based time series modeling.

Problem

• Bridging cross-modality gap between time series and LLMs
• Surveying LLM-based methods for time series analytics
• Evaluating strategies to enhance time series modeling with text

Innovation

• LLMs bridge time series and text modalities
• Cross-modality alignment and fusion strategies
• Multimodal datasets enhance time series analytics