Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DVS: Deep Visibility Series and its Application in Construction Cost
  Index Forecasting   Time series forecasting has always been a hot spot in economy. Visibility
Graph (VG) algorithm is used for time series prediction in previous research,
but the prediction effect is not as good as deep learning prediction methods
such as Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and
Long Short-Term Memory Network (LSTM) prediction. The VG algorithm contains a
wealth of network information, but previous studies did not effectively use the
network information to make predictions, resulting in relatively large
prediction errors. In order to optimize the forecasting method based on VG,
this paper proposes the Deep Visibility Series (DVS) module through the bionic
design of VG and the expansion of the past research, which is the first time to
combine VG with bionic design. By applying the bionic design of biological
vision to VG, DVS has obtained superior forecast accuracy. At the same time,
this paper applies the DVS forecasting method to the construction cost index
forecast, which has practical significance.

20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org