Large Language Models for Time Series Analysis: Methodologies, Applications, and Emerging Challenges

Large language models (LLMs), pre-trained on massive corpora have driven remarkable breakthroughs in natural language processing and computer vision. Building upon these successes, recent studies have examined the potential of pre-trained LLMs for time series analysis (TSA), focusing on downstream tasks such as time series forecasting, classification, anomaly detection and imputation across domains including finance, traffic and energy. In this survey, we systematically categorize the roles of LLMs for TSA into three principal types and further abstract their unified workflows: (1) fine-tune-based inference engines, where LLMs serve as core models for TSA tasks, with workflows involving tokenization, prompt engineering, semantic alignment, and fine-tuning; (2) TSA-methods-based enhancers, where LLMs serve as auxiliary modules within TSA methods; (3) hybrid collaborators, representing emerging approaches that integrate LLMs with lightweight models and multi-agent systems to enable advanced collaborative solutions. Then, we systematically review how LLMs empower downstream tasks across diverse domains. Specifically, we organize these applications according to the underlying mechanisms through which LLMs augment domain-specific tasks. This approach provides clear insights into the multifaceted roles of LLMs in advancing TSA across industries. Finally, we analyze the principal challenges faced by LLMs for TSA, including limitations such as cross-domain generalization, privacy preservation, and multimodal data alignment. By establishing a rigorous foundation, we aim to guide future research efforts and foster methodological innovation within the field.