Neural Network Forecasting of the Production Level of Chinese Construction Industry

Authors

  • Dilli R. Aryal Harbin Institute of Technology, Harbin, 150001, China
  • Yao-wu Wang Harbin Institute of Technology, Harbin, 150001, China, Harbin Institute of Technology, Harbin, 150001, China

Abstract

Increased efforts have been devoted over the past several decades to the development and improvement of time series forecasting models. In this paper, we determine whether the forecasting performance of variables under study can be improved using neural network models. Among the best 10 retained networks, an MLP 3- layer network: 1:1-31-1:1 is selected as the ANN model with the minimum RMSE. The performance of the model is evaluated by comparing it with the ARIMA model. The root mean squared forecast error of the best neural network model is 49 per cent lower than the ARIMA model counterpart. It shows that the neural network yields significant forecast improvements. The gains in forecast accuracy seem to originate from the ability of neural networks to capture asymmetric relationships. This methodology has been applied to forecast the Chinese construction industry (CI). Since CI contributes to GDP considerably, it has an important and supportive role in the national economy of China. The empirical results show that the trend of steadily increasing production levels of CI implies a strong potential for future growth.

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Published

2003-08-01

How to Cite

Aryal, D. R., & Wang, Y.- wu. (2003). Neural Network Forecasting of the Production Level of Chinese Construction Industry. Journal of Comparative International Management, 6(2). Retrieved from https://journals.lib.unb.ca/index.php/JCIM/article/view/446

Issue

Vol. 6 No. 2 (2003)

Section

RESEARCH ARTICLES

License

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