  Time series forecasting is a hot spot in recent years. Visibility Graph (VG)
algorithm is used for time series forecasting in previous research, but the
forecasting effect is not as good as deep learning prediction methods such as
methods based on Artificial Neural Network (ANN), Convolutional Neural Network
(CNN) and Long Short-Term Memory Network (LSTM). The visibility graph generated
from specific time series contains abundant network information, but the
previous forecasting method did not effectively use the network information to
forecast, resulting in relatively large prediction errors. To optimize the
forecasting method based on VG, this article proposes the Deep Visibility
Series (DVS) module through the bionic design of VG and the expansion of the
past research. By applying the bionic design of biological vision to VG, DVS
has obtained superior forecasting accuracy. At the same time, this paper
applies the DVS forecasting method to the construction cost index forecast,
which has practical significance.
