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Modelling the demand for cement: The case of Poland and Spain

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper develops a new tool for forecasting the demand for cement and tests it on the data from Poland and Spain. Predicting the demand for cement is a key issue from the perspective of the cement manufacturers. Forecasting this demand helps businesses determine, among others, the level of production, future revenue stream and purchase of raw materials. The hybrid models employed in this paper consists of Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model and Artificial Neural Network (ANN). The SARIMAX model was initially used to forecast the demand for cement. The resulting forecasting errors were further corrected with ANN, which was built to account for the nonlinear tendencies that the SARIMAX technique could not identify. The forecasting errors from the hybrid model were compared with the errors from ARIMA-type and the ANN models working separately. The results indicate that the hybrid models outperform of the models used separately. If implemented, this methodology may become a powerful decisionmaking tool for cement industry.
Rocznik
Strony
69--83
Opis fizyczny
Bibliogr. 37 poz., rys., tab. wykr.
Twórcy
autor
  • Department of Building Materials Engineering, Faculty of Civil Engineering, Warsaw University of Technology, Al. Armii Ludowej 16, 00-637 Warsaw, Poland
  • Institute of Security and Global Affairs, Leiden University, P.O. Box 13228, 2501 EE The Hague, The Netherlands
  • Institute of Computer Science, Polish Academy of Sciences, Jana Kazimierza 5, 01-248 Warsaw, Poland
Bibliografia
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  • Bercu S., Proïa F.: A SARIMAX coupled modelling applied to individual load curves intraday forecasting. Journal of Applied Statistics 40 (2013), 1333-1348. http://dx.doi.org/10.1080/02664763.2013.785496
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  • Hwarng H.B.: Insights into neural-network forecasting of time series corresponding to ARMA(p;q) structures. Omega 29 (2001), 273-289.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d2a3c845-fe2a-44d1-99ec-d56985bfd526
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