Time-Prompt: Integrated Heterogeneous Prompts for Unlocking LLMs in Time Series Forecasting

ArXi:2506.17631v4 Announce Type: replace Time series forecasting aims to model temporal dependencies among variables for future state inference, holding significant importance and widespread applications in real-world scenarios. Although deep learning-based methods have achieved remarkable progress, they still exhibit suboptimal performance in long-term forecasting. Recent research nstrates that large language models (LLMs) achieve promising performance in time series forecasting, but this progress is still met with skepticism about whether LLMs are truly useful for this task.