Empowering Time Series Analysis with Large Language Models: A Survey

Understand

Recently, remarkable progress has been made over large language models (LLMs), demonstrating their unprecedented capability in varieties of natural language tasks.

• However, completely training a large general-purpose model from the scratch is challenging for time series analysis, due to the large volumes and varieties of time series data, as well as the non-stationarity that leads to concept drift impeding continuous model adaptation and re-training.
• Recent advances have shown that pre-trained LLMs can be exploited to capture complex dependencies in time series data and facilitate various applications.
• In this survey, we provide a systematic overview of existing methods that leverage LLMs for time series analysis.