Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs

In the fashion retail industry, a versatile sales forecasting system is crucial for fashion retailers. In order to avoid stock-out and maintain a high inventory fill rate, fashion retailers require specific and accurate sales forecasting systems. In this study, a hybrid method based on extreme learning machine model with the adaptive metrics of inputs is proposed for improving sales forecasting accuracy. The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and reduce the effect of the overfitting of networks. The proposed algorithms are validated using real POS data of three fashion retailers selling high-ended, medium and basic fashion items in Hong Kong. It was found that the proposed model is practical for fashion retail sales forecasting and outperforms the auto-regression (AR), artificial neural network (ANN), and extreme learning machine (ELM) models.

[1]  Guang-Bin Huang,et al.  Learning capability and storage capacity of two-hidden-layer feedforward networks , 2003, IEEE Trans. Neural Networks.

[2]  Shian-Chang Huang,et al.  Online option price forecasting by using unscented Kalman filters and support vector machines , 2008, Expert Syst. Appl..

[3]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[4]  Pei-Chann Chang,et al.  Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry , 2009, Knowl. Based Syst..

[5]  Paulo S. A. Freitas,et al.  Model combination in neural-based forecasting , 2006, Eur. J. Oper. Res..

[6]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[7]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[8]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[9]  Ching-Chin Chern,et al.  Designing a decision-support system for new product sales forecasting , 2010, Expert Syst. Appl..

[10]  Pankaj Singh,et al.  Suitability of different neural networks in daily flow forecasting , 2007, Appl. Soft Comput..

[11]  Fred Collopy,et al.  How effective are neural networks at forecasting and prediction? A review and evaluation , 1998 .

[12]  Yevgeniy V. Bodyanskiy,et al.  Neural network approach to forecasting of quasiperiodic financial time series , 2006, Eur. J. Oper. Res..

[13]  Tai-Yue Wang,et al.  Forecasting innovation performance via neural networks—a case of Taiwanese manufacturing industry , 2006 .

[14]  R. J. Kuo,et al.  Fuzzy neural networks with application to sales forecasting , 1999, Fuzzy Sets Syst..

[15]  K. Donohue.  Efficient Supply Contracts for Fashion Goods with Forecast Updating and Two Production Modes , 2000 .

[16]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .

[17]  Kumar Rajaram,et al.  The Benefits of Advance Booking Discount Programs: Model and Analysis , 2004, Manag. Sci..

[18]  Melissa M. Florance,et al.  Positioning Sales Forecasting for Better Results , 1994 .

[19]  Gilbert Owusu,et al.  Modular neural networks for recursive collaborative forecasting in the service chain , 2008, Knowl. Based Syst..

[20]  Peter L. Bartlett,et al.  For Valid Generalization the Size of the Weights is More Important than the Size of the Network , 1996, NIPS.

[21]  G. Sahoo,et al.  Flow forecasting for a Hawaii stream using rating curves and neural networks , 2006 .

[22]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[23]  A. Kai Qin,et al.  Evolutionary extreme learning machine , 2005, Pattern Recognit..

[24]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[25]  Yong Yu,et al.  Sales forecasting using extreme learning machine with applications in fashion retailing , 2008, Decis. Support Syst..

[26]  Christopher M. Miller,et al.  Applying case-based reasoning to forecasting retail sales , 1993 .

[27]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[28]  Charles W. Chase.  Ways to Improve Sales Forecasts , 1993 .

[29]  Chin-Tsai Lin,et al.  Artificial intelligence diagnosis algorithm for expanding a precision expert forecasting system , 2009, Expert Syst. Appl..

[30]  M. Holschneider,et al.  Adaptive metrics in the nearest neighbours method , 2008 .