Author(s)

Junshan Huang ¹, Longfei Lu ¹, Hongzhi Chen ¹, Yan Liang ²

Affiliation(s)

¹ School of Computer, Guangdong University of Petrochemical Technology, Maoming, Guangdong, China
² School of Science, Guangdong University of Petrochemical Technology, Maoming, Guangdong, China

Corresponding Author

Yan Liang

ABSTRACT

Rebar is one of the largest steel products in China.  Rebar is widely used in building, bridge, road and other civil engineering construction.  It is an indispensable structural material for infrastructure construction. To grasp the demand dynamics of rebar in the market reasonably and effectively has great significance in practice.  The prediction of rebar demand is conducive to deepening the supply-side structural reform of the rebar industry, as well as improving the supply and demand situation, and alleviating the overcapacity situation of the rebar industry. The investment strategy of rebar futures can be adjusted according to the prediction results of rebar demand. Many factors affect the demand of the rebar market. To accurately predict the demand of rebar, this paper will first preprocess the sample data and standardize the deviation. Then XGBOOST model is adopted to integrate multiple base decision trees. Since the decision tree has the characteristics of nonlinear fitting, it can accurately predict the demand of rebar and provide an effective method to solve the long-term backward construction foundation recommendation in China.

KEYWORDS

Demand for rebar, PEARSON correlation, Gaussian distribution, XGBoost model

CITE THIS PAPER

Junshan Huang, Longfei Lu, Hongzhi Chen, Yan Liang, Analysis of Domestic Rebar Demand Based on Pearson Correlation Coefficient and XGBoost Model. Numerical Algebra and Scientific Computing (2022) Vol. 1: 1-8. DOI: http://dx.doi.org/10.23977nasc.2022.010101.

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