From Time Series Analysis to Question Answering: A Survey in the LLM Era

A new survey examines how Large Language Models are transforming time series analysis by shifting from traditional task-specific forecasting toward a unified question-answering framework. The research proposes three alignment paradigms to bridge the gap between LLM capabilities and temporal data analysis, offering practical guidance for selecting appropriate methodologies across domains.

The convergence of large language models with time series analysis represents a significant methodological shift in how we process and interpret temporal data. Traditionally, time series analysis has relied on specialized statistical and machine learning models optimized for specific tasks like forecasting or anomaly detection. LLMs introduce a new capability: leveraging natural language understanding to make temporal data analysis more accessible and interpretable to non-expert users through conversational interfaces.

This evolution stems from LLMs' inherent strength in language-based reasoning and their ability to handle multiple tasks within a unified framework. Rather than training separate models for forecasting, anomaly detection, and interpretation, TSQA enables a single system to answer diverse questions about temporal patterns. This democratizes access to time series insights, reducing the need for domain expertise in statistical modeling.

For the AI industry, this development has significant implications. Organizations can deploy more flexible analytical systems that adapt to user needs rather than requiring predefined analysis pipelines. The three proposed alignment paradigms—Injective, Bridging, and Internal Alignment—provide practical frameworks for implementation, enabling cost-effective deployment across different computational constraints and use cases.

Looking ahead, the critical challenge involves ensuring TSQA systems maintain accuracy while providing natural language interpretability. Researchers must develop robust evaluation methods and address domain-specific requirements, particularly in sectors like finance and healthcare where accuracy is paramount. The success of this paradigm will depend on whether unified question-answering approaches can match or exceed specialized model performance while maintaining computational efficiency.

• →LLMs enable shifting time series analysis from task-specific expert-driven approaches to unified user-driven question-answering systems.
• →Three alignment paradigms provide practical frameworks for integrating LLM capabilities with temporal data analysis at different scales.
• →TSQA democratizes time series insights by reducing the need for statistical expertise and enabling conversational data exploration.
• →The gap between LLM optimization objectives and time series analysis requirements remains a fundamental challenge requiring methodological innovation.
• →Domain-specific evaluation and accuracy maintenance are critical for TSQA adoption in regulated industries like finance and healthcare.