Multi-Factor Spatio-Temporal Prediction based on Graph Decomposition Learning

arXiv:2310.10374v2 Announce Type: replace
Abstract: Spatio-temporal (ST) prediction is an important and widely used technique in data mining and analytics, especially for ST data in urban systems such as transportation data. In practice, the ST data generation is usually influenced by various latent factors tied to natural phenomena or human socioeconomic activities, impacting specific spatial areas selectively. However, existing ST prediction methods usually do not refine the impacts of different factors, but directly model the entangled impacts of multiple factors. …

abstract analytics arxiv cs.lg data data mining graph human mining natural practice prediction systems temporal transportation type urban