Forecasting of the Demand of Alumina Based on the Coupling Phase-space Reconstruction and Neural Network
With the increasingly drastic competition in the market, consumers’ minds are more and more complex, and thedemand fluctuation of product is more and more frequent, and the demand forecasting is more and moreimportant for the management decision-making for enterprises. Based on the phase-space reconstruction of theoriginal demand data by the chaos theory, the reconstructed data are trained in the neural network (NN), and theforecasting times are selected to forecast the development tendency of the demand, and the research result isfinally tested by the alumina demand data from 2001 to 2009 of an alumina factory. The result shows that thismodel is simple and easy to operate, and the forecasting data are reliable, and it could offer theoretical referencesfor management decision-makers to make scientific and reasonable decisions.