Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recurrent Neural Networks for Time Series Forecasting: Current Status
  and Future Directions   Recurrent Neural Networks (RNN) have become competitive forecasting methods,
as most notably shown in the winning method of the recent M4 competition.
However, established statistical models such as ETS and ARIMA gain their
popularity not only from their high accuracy, but they are also suitable for
non-expert users as they are robust, efficient, and automatic. In these areas,
RNNs have still a long way to go. We present an extensive empirical study and
an open-source software framework of existing RNN architectures for
forecasting, that allow us to develop guidelines and best practices for their
use. For example, we conclude that RNNs are capable of modelling seasonality
directly if the series in the dataset possess homogeneous seasonal patterns,
otherwise we recommend a deseasonalization step. Comparisons against ETS and
ARIMA demonstrate that the implemented (semi-)automatic RNN models are no
silver bullets, but they are competitive alternatives in many situations.

20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio