Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: New Results on Multi-Step Traffic Flow Prediction   In its simplest form, the traffic flow prediction problem is restricted to
predicting a single time-step into the future. Multi-step traffic flow
prediction extends this set-up to the case where predicting multiple time-steps
into the future based on some finite history is of interest. This problem is
significantly more difficult than its single-step variant and is known to
suffer from degradation in predictions as the time step increases. In this
paper, two approaches to improve multi-step traffic flow prediction performance
in recursive and multi-output settings are introduced. In particular, a model
that allows recursive prediction approaches to take into account the temporal
context in term of time-step index when making predictions is introduced. In
addition, a conditional generative adversarial network-based data augmentation
method is proposed to improve prediction performance in the multi-output
setting. The experiments on a real-world traffic flow dataset show that the two
methods improve on multi-step traffic flow prediction in recursive and
multi-output settings, respectively.

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The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
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The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: q-bio