  Forecasting graph-based time-dependent data has many practical applications.
This task is challenging as models need not only to capture spatial dependency
and temporal dependency within the data, but also to leverage useful auxiliary
information for accurate predictions. In this paper, we analyze limitations of
state-of-the-art models on dealing with temporal dependency. To address this
limitation, we propose GSA-Forecaster, a new deep learning model for
forecasting graph-based time-dependent data. GSA-Forecaster leverages graph
sequence attention (GSA), a new attention mechanism proposed in this paper, for
effectively capturing temporal dependency. GSA-Forecaster embeds the graph
structure of the data into its architecture to address spatial dependency.
GSA-Forecaster also accounts for auxiliary information to further improve
predictions. We evaluate GSA-Forecaster with large-scale real-world graph-based
time-dependent data and demonstrate its effectiveness over state-of-the-art
models with 6.7% RMSE and 5.8% MAPE reduction.
