Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn