Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning

arXiv:2405.03255v1 Announce Type: new
Abstract: Multi-modality spatio-temporal (MoST) data extends spatio-temporal (ST) data by incorporating multiple modalities, which is prevalent in monitoring systems, encompassing diverse traffic demands and air quality assessments. Despite significant strides in ST modeling in recent years, there remains a need to emphasize harnessing the potential of information from different modalities. Robust MoST forecasting is more challenging because it possesses (i) high-dimensional and complex internal structures and (ii) dynamic heterogeneity caused by temporal, spatial, and modality variations. …

arxiv cs.lg forecasting self-supervised learning supervised learning temporal type via