DVS: Deep Visibility Series and its Application in Construction Cost Index Forecasting

TL;DR

This paper introduces the Deep Visibility Series (DVS), a novel forecasting method that enhances visibility graph-based time series prediction by incorporating biological vision principles, significantly improving accuracy in construction cost index forecasting.

Contribution

The paper proposes the DVS module, which optimizes visibility graph forecasting using bionic design, and demonstrates its effectiveness in practical construction cost index prediction.

Findings

DVS achieves higher forecasting accuracy than traditional VG methods.

Application of DVS to construction cost index shows practical forecasting improvements.

Bionic design enhances network information utilization in time series prediction.

Abstract

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.