3 research outputs found
Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models
Multivariate signals are prevalent in various domains, such as healthcare,
transportation systems, and space sciences. Modeling spatiotemporal
dependencies in multivariate signals is challenging due to (1) long-range
temporal dependencies and (2) complex spatial correlations between sensors. To
address these challenges, we propose representing multivariate signals as
graphs and introduce GraphS4mer, a general graph neural network (GNN)
architecture that captures both spatial and temporal dependencies in
multivariate signals. Specifically, (1) we leverage Structured State Spaces
model (S4), a state-of-the-art sequence model, to capture long-term temporal
dependencies and (2) we propose a graph structure learning layer in GraphS4mer
to learn dynamically evolving graph structures in the data. We evaluate our
proposed model on three distinct tasks and show that GraphS4mer consistently
improves over existing models, including (1) seizure detection from
electroencephalography signals, outperforming a previous GNN with
self-supervised pretraining by 3.1 points in AUROC; (2) sleep staging from
polysomnography signals, a 4.1 points improvement in macro-F1 score compared to
existing sleep staging models; and (3) traffic forecasting, reducing MAE by
8.8% compared to existing GNNs and by 1.4% compared to Transformer-based
models