Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Base Station Network Traffic Prediction Approach Based on LMA-DeepAR   Accurate network traffic prediction of base station cell is very vital for
the expansion and reduction of wireless devices in base station cell. The burst
and uncertainty of base station cell network traffic makes the network traffic
nonlinear and non-stationary, which brings challenges to the long-term
prediction of network traffic. In this paper, the traffic model LMA-DeepAR for
base station network is established based on DeepAR. Acordding to the
distribution characteristics of network traffic, this paper proposes an
artificial feature sequence calculation method based on local moving average
(LMA). The feature sequence is input into DeepAR as covariant, which makes the
statistical characteristics of network traffic near a period of time in the
past be considered when updating parameters, and the interference of
non-stationary network traffic on model training will be reduced. Experimental
results show that the proposed prediction approach (LMA-DeepAR) outperforms
other methods in the overall long-term prediction performance and stability of
multi cell network traffic.

25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th