  Spatiotemporal (ST) data collected by sensors can be represented as
multi-variate time series, which is a sequence of data points listed in an
order of time. Despite the vast amount of useful information, the ST data
usually suffer from the issue of missing or incomplete data, which also limits
its applications. Imputation is one viable solution and is often used to
prepossess the data for further applications. However, in practice, n practice,
spatiotemporal data imputation is quite difficult due to the complexity of
spatiotemporal dependencies with dynamic changes in the traffic network and is
a crucial prepossessing task for further applications. Existing approaches
mostly only capture the temporal dependencies in time series or static spatial
dependencies. They fail to directly model the spatiotemporal dependencies, and
the representation ability of the models is relatively limited.
