Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Experimental Review on Deep Learning Architectures for Time Series
  Forecasting   In recent years, deep learning techniques have outperformed traditional
models in many machine learning tasks. Deep neural networks have successfully
been applied to address time series forecasting problems, which is a very
important topic in data mining. They have proved to be an effective solution
given their capacity to automatically learn the temporal dependencies present
in time series. However, selecting the most convenient type of deep neural
network and its parametrization is a complex task that requires considerable
expertise. Therefore, there is a need for deeper studies on the suitability of
all existing architectures for different forecasting tasks. In this work, we
face two main challenges: a comprehensive review of the latest works using deep
learning for time series forecasting; and an experimental study comparing the
performance of the most popular architectures. The comparison involves a
thorough analysis of seven types of deep learning models in terms of accuracy
and efficiency. We evaluate the rankings and distribution of results obtained
with the proposed models under many different architecture configurations and
training hyperparameters. The datasets used comprise more than 50000 time
series divided into 12 different forecasting problems. By training more than
38000 models on these data, we provide the most extensive deep learning study
for time series forecasting. Among all studied models, the results show that
long short-term memory (LSTM) and convolutional networks (CNN) are the best
alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve
comparable performance with less variability of results under different
parameter configurations, while also being more efficient.

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The categories of this paper are: physics
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
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The categories of this paper are: econ
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th