Gray relation analysis and multilayer functional link network sales forecasting model for perishable food in convenience store

In managing convenience store, making the right decision in placing a balanced order is a critical job that can enhance the competition of the corporation, especially in perishable food. In this study, the GMFLN forecasting model integrates Gray relation analysis (GRA) which sieves out the more influential factors from raw data then transforms them as the input data in the multilayer functional link network model to provide the more accurate forecasting results to support the decisions. The proposed system evaluated the real data, which are provided by famous franchise company, and the experimental results indicated the GMFLN model outperforms than other different time series forecasting models, i.e. the moving average model, ARIMA model and GARCH model in MAD and THEIL indexes.

[1]  Suh Young Kang,et al.  An investigation of the use of feedforward neural networks for forecasting , 1992 .

[2]  R. Radharamanan,et al.  Sales forecasting using time series and neural networks , 1996 .

[3]  David E. Rumelhart,et al.  Generalization by Weight-Elimination with Application to Forecasting , 1990, NIPS.

[4]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[5]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[6]  Paul A. Fishwick,et al.  Time series forecasting using neural networks vs. Box- Jenkins methodology , 1991, Simul..

[7]  Joseph P. Bigus,et al.  Data mining with neural networks: solving business problems from application development to decision support , 1996 .

[8]  J. David Fuller,et al.  Back propagation in time‐series forecasting , 1995 .

[9]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[10]  Pei-Chann Chang,et al.  Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry , 2006, Expert Syst. Appl..

[11]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[12]  Clive W. J. Granger,et al.  Strategies for Modelling Nonlinear Time‐Series Relationships* , 1993 .

[13]  R. J. Kuo,et al.  A sales forecasting system based on fuzzy neural network with initial weights generated by genetic algorithm , 2001, Eur. J. Oper. Res..

[14]  Guoqiang Peter Zhang,et al.  An investigation of neural networks for linear time-series forecasting , 2001, Comput. Oper. Res..

[15]  H. Akaike.  A new look at the statistical model identification , 1974 .

[16]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[17]  D. Agrawal,et al.  Market share forecasting: An empirical comparison of artificial neural networks and multinomial logit model , 1996 .

[18]  Milton S. Boyd,et al.  Designing a neural network for forecasting financial and economic time series , 1996, Neurocomputing.

[19]  J. A. Chen,et al.  A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm , 2004, Expert Syst. Appl..

[20]  Gary S. LeVee.  The Key to Understanding the Forecasting Process , 1993 .