Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Network Forecast of the Sunspot Butterfly Diagram   Using neural networks as a prediction method, we attempt to demonstrate that
forecasting of the Sun's sunspot time series can be extended to the
spatial-temporal case. We employ this machine learning methodology to forecast
not only in time but also in space (in this case the latitude) on a
spatial-temporal dataset representing the solar sunspot diagram extending to a
total of 142 years. The analysis shows that this approach seems to be able to
reconstruct the overall qualitative aspects of the spatial-temporal series,
namely the overall shape and amplitude of the latitude and time pattern of
sunspots. This is, as far as we are aware, the first time neural networks have
been used to forecast the Sun's sunspot butterfly diagram, and although the
results are limited in the quantitative prediction aspects, it points the way
to use the full spatial-temporal series as opposed to just the time series for
machine learning approaches to forecasting. Further to that, we use the method
to predict that the upcoming Cycle 25 maximum sunspot number will be around
$R_{25}=57 \pm 17$. This implies a very weak cycle and that it will be the
weakest cycle on record.

19
The categories of this paper are: eess
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom