Liu, C.M. (2009) Price Forecast for Gold Futures Based on GA-BP Neural Network. 2009 International Conference on Management and Service Science, 1-4. https://doi.org/10.1109/ICMSS.2009.5302242

has been cited by the following article:

• TITLE: A Look-Ahead Method for Forecasting the Concrete Price

  AUTHORS: Qing Liu, Minghao Huang, Woon-Seek Lee

  KEYWORDS: Price Forecasting, Concrete, Deep Learning, Autoregression

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.10 No.5, May 31, 2022

  ABSTRACT: Price movement of building materials increases the uncertainty of architectural planning. As a basic building material, commercial concrete is an important part of various construction costs. It is of great significance to predict its price change trend in advance. In this paper, a univariate autoregressive series is constructed based on the daily average price of concrete in major cities in China; then it uses a combined model of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) to extract the spatial and temporal rules of time series, to achieve accurate prediction of the trend of concrete price changes 10 days ago. The prediction accuracy rate of the model is 97.13%, and the precision, recall rate, and F1 score are: 97.15%, 97.27%, and 97.20%, respectively. The prediction result is of great significance to various architectural planning.