Prediction of Women’s Fashion Product Expense in Hanoi City Using Artificial Neural Network
Main Article Content
Abstract
A person's fashion product expense plays an important role in building a garment company's product and pricing strategy. This article presents the results of predicting the fashion expenses of women aged 25 to 55 in Hanoi city. A linear multivariable model determined by the Bayesion Model Average (BMA) was applied to predict expenditures based on a set of 150 women survey data. The 3 layers feed-forward neural network model with 8 input neurons, 4 (+1) hidden layer neurons, and 1 output layer neuron, The Sigmoid activation function, trained by the error backpropagation algorithm has been established on R software specifically predicts the value of women's fashion expense with the above data set. The results show that the artificial neural network (ANN) has been set up allowing to prediction of fashion expenses accurately. The expenditure values predicted by the ANN and the actual value are linearly correlated with R2 = 0.987; The prediction results by ANN are more accurate than the defined multivariable linear model.
Keywords
Artificial neural network, Expense for fashion products, Prediction
Article Details
References
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[4] L. Sztandera, C. Frank, and B. Vemulapali, Prediction of women’s apparel sales using soft computing methods, Lect Notes Artif Intell, 3215: 506-512, 2004. https://doi.org/10.1007/978-3-540-30134-9_68
[5] S. Thomassey, M. Happiette, and J. Castelain, A global forecasting support system adapted to textile distribution, Int J Prod Econ, 96: 81-95, 2005. https://doi.org/10.1016/j.ijpe.2004.03.001
[6] S. Thomassey, and A. Fiordaliso, A hybrid sales forecasting system based on clustering and decision trees, Decis Support Syst, 42: 408-421, 2006. https://doi.org/10.1016/j.dss.2005.01.008
[7] Z. Sun, T. Choi, K. Au, and Y. Yu, Sales forecasting using extreme learning machine with applications in fashion retailing, Decis Support Syst, 46: 411-419, 2008. https://doi.org/10.1016/j.dss.2008.07.009
[8] A. Aksoy, N. Öztürk, and E. Sucky, Demand forecasting for apparel manufacturers by using neuro-fuzzy techniques, Journal of Modelling in Management, vol. 9 Issue: 1, pp.18-35, 2014. https://doi.org/10.1108/JM2-10-2011-0045
[9] Z. X. Guo, W. K. Wong, S. Y. S. Leung, J. T. Fan, and S. F. Chan, A genetic-algorithm-based optimization model for solving the flexible assembly line balancing problem with work sharing and workstation revisiting, IEEE Trans Syst Man Cy C, 38: 218-228, 2008. https://doi.org/10.1109/TSMCC.2007.913912
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[11] F. Dominici, J. J. Faraway, M. Tanner, and J. Zidek, Linear Models with R, CHAPMAN & HALL/CRC, CRC Press Taylor & Francis Group, 2014.
[12] Giuseppe Ciaburro, Balaji Venkateswaran, Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles, Published by Packt Publishing Ltd., Birmingham B3 2PB, UK, 2017.
[13] M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design, PWS Publishing Company, 1996.
[14] Nguyen Thi Le, Ngo Chi Trung, Le Huu Chien, Seam pucker prediction based on fabric structure and mechanical properties using neural network, Journal of Science and Technology, No.65, 2008.