Forecasting of the Demand of Alumina Based on the Coupling Phase-space Reconstruction and Neural Network

Xiaojun Yan, Jianchuan Luo, Zhiya Chen

Abstract


With the increasingly drastic competition in the market, consumers’ minds are more and more complex, and the
demand fluctuation of product is more and more frequent, and the demand forecasting is more and more
important for the management decision-making for enterprises. Based on the phase-space reconstruction of the
original demand data by the chaos theory, the reconstructed data are trained in the neural network (NN), and the
forecasting times are selected to forecast the development tendency of the demand, and the research result is
finally tested by the alumina demand data from 2001 to 2009 of an alumina factory. The result shows that this
model is simple and easy to operate, and the forecasting data are reliable, and it could offer theoretical references
for management decision-makers to make scientific and reasonable decisions.

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International Journal of Business and Management   ISSN 1833-3850 (Print)   ISSN 1833-8119 (Online)

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