Forecasting Determinant of Cement Demand in Indonesia with Artificial Neural Network

Edy Fradinata, Sakesun Suthummanon and Wannarat Suntiamorntut

Journal of Asian Scientific Research, 2015, vol. 5, issue 7, 373-384

Abstract: This paper was presented Artificial Neural Network (ANN) as one of the predicting methods to obtain more accurate predicted data. Several methods have been applied to this purpose but still not gave a better accuracy. The method could be used in linear and nonlinear characteristic of data. Back propagation neural network was implemented in this experiment to solve the predicting problem. This research proposed to predict some future points to get the advantages from it. The data was demonstrated in generate from determinant of cement demand in Indonesia region. The data was used 6 variables which influenced the demand factors. The contribution of this experiment was an exploring the accuracy of predicted and the future points value of data. The result of this experiment was: data A, B, C, D, E and F data had the range of MSE 6.23e-9 until 1.34e-7. The MSE for the actual data was 2.39e-6 and the predicted was 1.99e-6. The predicting points has resulted on months 91 until 97 were 0.5854, 0.8448, 0.510, 0.6462, 0.8528, 0.516 and 0.5074 respectively. The delta between predicted and actual data were 0.0066, 0.1418, 0.069, 0.1492, 0.2038, 0.355, and 0.1046. The result of this experiment could be used to predict the future months on the time frame with neural network and measured the MSE of its performance.

Keywords: Artificial neural network (ANN); Determinant of cement demand; MSE; Cement industry; Predicting; Forecasting; Linear-nonlinear; Time series. (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
https://archive.aessweb.com/index.php/5003/article/view/3737/5906 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:asi:joasrj:v:5:y:2015:i:7:p:373-384:id:3737

Access Statistics for this article

More articles in Journal of Asian Scientific Research from Asian Economic and Social Society
Bibliographic data for series maintained by Robert Allen ().