Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm   Due to their prevalence, time series forecasting is crucial in multiple
domains. We seek to make state-of-the-art forecasting fast, accessible, and
generalizable. ES-RNN is a hybrid between classical state space forecasting
models and modern RNNs that achieved a 9.4% sMAPE improvement in the M4
competition. Crucially, ES-RNN implementation requires per-time series
parameters. By vectorizing the original implementation and porting the
algorithm to a GPU, we achieve up to 322x training speedup depending on batch
size with similar results as those reported in the original submission. Our
code can be found at: https://github.com/damitkwr/ESRNN-GPU

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The categories of this paper are: physics
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The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th