  Spatio-temporal (ST) data for urban applications, such as taxi demand,
traffic flow, regional rainfall is inherently stochastic and unpredictable.
Recently, deep learning based ST prediction models are proposed to learn the ST
characteristics of data. However, it is still very challenging (1) to
adequately learn the complex and non-linear ST relationships; (2) to model the
high variations in the ST data volumes as it is inherently dynamic, changing
over time (i.e., irregular) and highly influenced by many external factors,
such as adverse weather, accidents, traffic control, PoI, etc.; and (3) as
there can be many complicated external factors that can affect the accuracy and
it is impossible to list them explicitly. To handle the aforementioned issues,
in this paper, we propose a novel deep generative adversarial network based
model (named, D-GAN) for more accurate ST prediction by implicitly learning ST
feature representations in an unsupervised manner. D-GAN adopts a GAN-based
structure and jointly learns generation and variational inference of data. More
specifically, D-GAN consists of two major parts: (1) a deep ST feature learning
network to model the ST correlations and semantic variations, and underlying
factors of variations and irregularity in the data through the implicit
distribution modelling; (2) a fusion module to incorporate external factors for
reaching a better inference. To the best our knowledge, no prior work studies
ST prediction problem via deep implicit generative model and in an unsupervised
manner. Extensive experiments performed on two real-world datasets show that
D-GAN achieves more accurate results than traditional as well as deep learning
based ST prediction methods.
