@article{(Open Science Index):https://publications.waset.org/pdf/7938,
	  title     = {Consumer Product Demand Forecasting based on Artificial Neural Network and Support Vector Machine},
	  author    = {Karin Kandananond},
	  country	= {},
	  institution	= {},
	  abstract     = {The nature of consumer products causes the difficulty
in forecasting the future demands and the accuracy of the forecasts
significantly affects the overall performance of the supply chain
system. In this study, two data mining methods, artificial neural
network (ANN) and support vector machine (SVM), were utilized to
predict the demand of consumer products. The training data used was
the actual demand of six different products from a consumer product
company in Thailand. The results indicated that SVM had a better
forecast quality (in term of MAPE) than ANN in every category of
products. Moreover, another important finding was the margin
difference of MAPE from these two methods was significantly high
when the data was highly correlated.},
	    journal   = {International Journal of Economics and Management Engineering},
	  volume    = {6},
	  number    = {3},
	  year      = {2012},
	  pages     = {313 - 316},
	  ee        = {https://publications.waset.org/pdf/7938},
	  url   	= {https://publications.waset.org/vol/63},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 63, 2012},
	}