This work introduces the LLM Online Spatial-temporal Reconstruction (LLM-OSR)
framework, which integrates Graph Signal Processing (GSP) and Large Language
Models (LLMs) for online spatial-temporal signal reconstruction. The LLM-OSR
utilizes a GSP-based spatial-temporal signal handler to enhance graph signals
and employs LLMs to predict missing values based on spatiotemporal patterns.
The performance of LLM-OSR is evaluated on traffic and meteorological datasets
under varying Gaussian noise levels. Experimental results demonstrate that
utilizing GPT-4-o mini within the LLM-OSR is accurate and robust under Gaussian
noise conditions. The limitations are discussed along with future research
insights, emphasizing the potential of combining GSP techniques with LLMs for
solving spatiotemporal prediction tasks.
