  Traditional methods for demand forecasting only focus on modeling the
temporal dependency. However, forecasting on spatio-temporal data requires
modeling of complex nonlinear relational and spatial dependencies. In addition,
dynamic contextual information can have a significant impact on the demand
values, and therefore needs to be captured. For example, in a bike-sharing
system, bike usage can be impacted by weather. Existing methods assume the
contextual impact is fixed. However, we note that the contextual impact evolves
over time. We propose a novel context integrated relational model, Context
Integrated Graph Neural Network (CIGNN), which leverages the temporal,
relational, spatial, and dynamic contextual dependencies for multi-step ahead
demand forecasting. Our approach considers the demand network over various
geographical locations and represents the network as a graph. We define a
demand graph, where nodes represent demand time-series, and context graphs (one
for each type of context), where nodes represent contextual time-series.
Assuming that various contexts evolve and have a dynamic impact on the
fluctuation of demand, our proposed CIGNN model employs a fusion mechanism that
jointly learns from all available types of contextual information. To the best
of our knowledge, this is the first approach that integrates dynamic contexts
with graph neural networks for spatio-temporal demand forecasting, thereby
increasing prediction accuracy. We present empirical results on two real-world
datasets, demonstrating that CIGNN consistently outperforms state-of-the-art
baselines, in both periodic and irregular time-series networks.
