Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading
  Deep learning applies hierarchical layers of hidden variables to construct
nonlinear high dimensional predictors. Our goal is to develop and train deep
learning architectures for spatio-temporal modeling. Training a deep
architecture is achieved by stochastic gradient descent (SGD) and drop-out (DO)
for parameter regularization with a goal of minimizing out-of-sample predictive
mean squared error. To illustrate our methodology, we predict the sharp
discontinuities in traffic flow data, and secondly, we develop a classification
rule to predict short-term futures market prices as a function of the order
book depth. Finally, we conclude with directions for future research.
