LLM Online Spatial-temporal Signal Reconstruction Under Noise
This work addresses spatiotemporal prediction tasks, such as traffic and weather forecasting, by combining existing techniques in a novel way, though it appears incremental in nature.
The paper tackles the problem of online spatial-temporal signal reconstruction under noise by integrating Graph Signal Processing and Large Language Models, achieving accurate and robust performance on traffic and meteorological datasets with Gaussian noise.
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.