Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling

arXiv:2402.10634v1 Announce Type: new
Abstract: Given a set of synchronous time series, each associated with a sensor-point in space and characterized by inter-series relationships, the problem of spatiotemporal forecasting consists of predicting future observations for each point. Spatiotemporal graph neural networks achieve striking results by representing the relationships across time series as a graph. Nonetheless, most existing methods rely on the often unrealistic assumption that inputs are always available and fail to capture hidden spatiotemporal dynamics when part of the …

abstract arxiv cs.ai cs.lg data downsampling forecasting future graph graph-based graph neural networks networks neural networks relationships sensor series set space through time series type