  Spatial and time-dependent data is of interest in many applications. This
task is difficult due to its complex spatial dependency, long-range temporal
dependency, data non-stationarity, and data heterogeneity. To address these
challenges, we propose Forecaster, a graph Transformer architecture.
Specifically, we start by learning the structure of the graph that
parsimoniously represents the spatial dependency between the data at different
locations. Based on the topology of the graph, we sparsify the Transformer to
account for the strength of spatial dependency, long-range temporal dependency,
data non-stationarity, and data heterogeneity. We evaluate Forecaster in the
problem of forecasting taxi ride-hailing demand and show that our proposed
architecture significantly outperforms the state-of-the-art baselines.
