Spatio-temporal Forecasting
Spatio-temporal forecasting is a cornerstone of logistics, urban planning, and intelligent transportation systems.
However, constrained by deployment costs and maintenance resources, sensor networks often lack comprehensive spatial coverage, rendering Forecast Unobserved Node States (FUNS) a critical yet formidable challenge.
Conventional Models Limitations
Conventional models rely on historical observations and typically falter when encountering nodes without prior records.
GenST Framework Proposal
To address this, we redefine the problem as a conditional generation task on spatio-temporal graphs and propose GenST, a framework that introduces Large Language Models (LLMs) as a semantic bridge.
Leveraging a pre-trained LLM fine-tuned to extract rich semantic features from node descriptions, such as functional zones and road network structures, to compensate for missing spatio-temporal signals.
Two-Stage Generative Architecture
Specifically, we design a two-stage generative architecture:
- Spatio-Temporal VAE first compresses spatio-temporal dynamics into a latent space,
- followed by a Generative Transformer (GenT) that reconstructs the future states of unobserved nodes from noise,
guided by multi-modal conditions including semantics, geographic coordinates, and neighborhood contexts.
Experiments and Results
Experiments on six traffic and two non-traffic datasets show GenST significantly outperforms existing baselines in zero-shot prediction tasks,
demonstrating the practical potential of semantic-guided generation for mitigating spatio-temporal data sparsity.