Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Time Series Forecasting With Deep Learning: A Survey   Numerous deep learning architectures have been developed to accommodate the
diversity of time series datasets across different domains. In this article, we
survey common encoder and decoder designs used in both one-step-ahead and
multi-horizon time series forecasting -- describing how temporal information is
incorporated into predictions by each model. Next, we highlight recent
developments in hybrid deep learning models, which combine well-studied
statistical models with neural network components to improve pure methods in
either category. Lastly, we outline some ways in which deep learning can also
facilitate decision support with time series data.

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The categories of this paper are: atom-ph
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The categories of this paper are: q-fin
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
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The categories of this paper are: econ
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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: dg-ga
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: q-bio
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The categories of this paper are: gr-qc