Robust Spatio-Temporal Graph Convolutional Networks for Headcount Prediction

Ryusei Iwasa, Yuma Dose, Takahiro Hara, Takuya Maekawa, Kazuki Shimazaki, Teppei Seguchi, Takayuki Kikuchi, Kenshiro Kato

Abstract: Spatio-temporal forecasting addresses the prediction of phenomena that evolve over space and time. A key application is headcount prediction, which enables socially beneficial functions, such as the intelligent control of HVAC systems, by estimating occupancy within defined areas. Conventional headcount-prediction methods rely on sensor-derived data that are often corrupted by noise and missing values. Therefore, existing studies require extensive preprocessing, including data cleaning, to mitigate these issues. To overcome this limitation, we propose a robust spatio-temporal graph convolutional network (RSTGCN) that integrates a reliability-weighting module into the feature-extraction process, thereby attenuating the influence of unreliable measurements. Experiments show that our proposed model, RSTGCN, achieves superior forecasting performance on unprocessed real-world datasets containing noise and missing values.

External IDs:dblp:conf/ictai/IwasaDHMSSKK25