Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic
  Forecasting   Spatiotemporal forecasting has various applications in neuroscience, climate
and transportation domain. Traffic forecasting is one canonical example of such
learning task. The task is challenging due to (1) complex spatial dependency on
road networks, (2) non-linear temporal dynamics with changing road conditions
and (3) inherent difficulty of long-term forecasting. To address these
challenges, we propose to model the traffic flow as a diffusion process on a
directed graph and introduce Diffusion Convolutional Recurrent Neural Network
(DCRNN), a deep learning framework for traffic forecasting that incorporates
both spatial and temporal dependency in the traffic flow. Specifically, DCRNN
captures the spatial dependency using bidirectional random walks on the graph,
and the temporal dependency using the encoder-decoder architecture with
scheduled sampling. We evaluate the framework on two real-world large scale
road network traffic datasets and observe consistent improvement of 12% - 15%
over state-of-the-art baselines.

3
The categories of this paper are: physics
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
37
The categories of this paper are: supr-con