**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**30140

##### Validation and Selection between Machine Learning Technique and Traditional Methods to Reduce Bullwhip Effects: a Data Mining Approach

**Authors:**
Hamid R. S. Mojaveri,
Seyed S. Mousavi,
Mojtaba Heydar,
Ahmad Aminian

**Abstract:**

**Keywords:**
Artificial Neural Networks (ANN),
bullwhip effect,
demand forecasting,
Support Vector Machine (SVM).

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.1060157

**References:**

[1] C. W. J. Granger, "Can we improve the perceived quality of economic forecasts?", J. Appl. Econom.,. Vol. 11, 1996, pp. 455-473.

[2] P. H. Franses, G. Draisma, "Recognizing changing seasonal patterns using artificial neural networks", J. Econometrics, Vol. 81, 1997, pp. 273-280.

[3] R. T. Peterson, "Forecasting practices in the retail industry", J. Bus. Forec., vol. 12, 1993, pp. 11-14.

[4] M. Qi, G. S. Maddala, "Economic factors and the stock market: a new perspective", J. Forec., vol. 18 no. 3, 1999, pp. 151-166.

[5] M. Qi, "Nonlinear predictability of stock returns using financial and economic variables", J. Bu. Econom. Statist., vol. 17, no. 4, 1999, pp. 419-429.

[6] M. Qi, "Financial applications of artificial neural networks", in Handbook of Statistics, Statistical Methods in Finance, vol. 4, G. S. Maddala, C. R. Rao, Ed. North-Holland, Elsevier Science Publishers, Amsterdam, 1996, pp. 529-552.

[7] K. A. Krycha, U. Wagner, "Applications of artificial neural networks in management science: a survey", J. Retail. Cons. Ser., vol. 6, 1999, pp. 185-203.

[8] G. P. Zhang, B. E. Patuwo, M. Y. Hu, "Forecasting with artificial neural networks: the state of the art", Intl. J. Forec., vol. 14, no. 1, 1998, pp. 35-62.

[9] B. Jeong, H. Jung, N. Park, "A computerized causal forecasting system using genetic algorithms in supply chain management", J. Sys. Software, vol. 60, 2002, pp. 223-237.

[10] T. R. Willemain, C. N. Smart, H. F. Schwarz, "A new approach to forecasting intermittent demand for service parts inventories", Intl. J. Forec., vol. 20, 2004, pp. 375- 387.

[11] C. S. Hilas, S. K. Goudos, J. N. Sahalos, "Seasonal decomposition and forecasting of telecommunication data: A comparative case study", Technol. Forec. Soc. Change, vol. 73, 2006, pp. 495-509.

[12] S. Kang, "An investigation of the use of feedforward neural networks for forecasting", Ph.D. Dissertation, Kent State University, Kent, Ohio, 1991.

[13] T. Hill, M. O'Connor, W. Remus, "Neural network models for time series forecast", Manag. Sci., vol. 42, no. 7, 1996, pp. 1082-1092.

[14] M. Nelson, T. Hill, B. Remus, M. O'Connor, "Can neural networks be applied to time series forecasting learn seasonal patterns: an empirical investigation", in Proc. 27th Ann. Int. Conf. System Sciences, Hawaii, 1994, pp. 649-655.

[15] B. Foster, F. Collopy, L. Ungar, "Neural network forecasting of short, noisy time series", Comput. Chem. Eng., vol. 16, no. 12, 1992, pp. 293- 297.

[16] M. Dugan, K. A. Shriver, A. Peter, "How to forecast income statement items for auditing purposes", J. Bus. Forec., vol. 13, 1994, pp. 22-26.

[17] I. Alon, "Forecasting aggregate retail sales: the Winters' model revisited", in The 1997 Annual Proceedings, J. C. Goodale, Ed., Midwest Decision Science Institute, 1997, pp. 234-236.

[18] I. Alon, Q. Min, R. Sadowski, "Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional method", J. Retail. Cons. Ser., vol. 8, 2001, pp. 147-156.

[19] T. M. O'Donovan, Short Term Forecasting: An Introduction to the Box- Jenkins Approach. NY: New York, Wiley, 1983.

[20] R. Sharda, R. Patil, "Connectionist approach to time series prediction: an empirical test", J. Intell. Manufac., vol. 3, 1992, pp.317-323.

[21] Z. Tang, C. de Almeida, P. Fishwick, "Time series forecasting using neural networks vs. Box-Jenkins methodology", Simulation, vol. 57, no. 5, 1990, pp.303-310.

[22] V. Vapnik, The Nature of Statistical Learning Theory. New York, Springer, 1995.

[23] V. Vapnik, S. E. Golowich, A. Smola, "Support vector method for function approximation, regression estimation, and signal processing", Adv. Neural inf. Process. Syst., vol. 9, 1997, pp. 287-291.

[24] S. Mukherjee, E. Osuna, F. Girosi, "Nonlinear prediction of chaotic time series using support vector machines", in Proc. IEEE NNSP, 1997.

[25] S. Ruping, K. Morik, "Support Vector Machines and Learning about Time", in Proc. of ICASSP, 2003.

[26] J. Han, M. Kamber, Data Preprocessing, Data Mining, concepts and techniques, 2nd ed., Morgan Kaufman Publisher, 2006, ch2.

[27] T. Calinski, J. Harabasz, "A Dendrite Method for Cluster Analysis", Commun. Statist., Vol. 3, 1974, pp. 1-27.

[28] K. Hornik, M. Stinchcombe, H. White, "Multilayer feedforward networks are universal approximators", Neural Net., vol. 2, 1989, pp. 359-366.

[29] K. Hornik, M. Stinchcombe, H. White, "Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks", Neural Net., vol. 3, 1990, pp. 551-560.

[30] H. White, "Connectionist nonparametric regression: multilayer feedforward networks can learn arbitrary mappings", Neural Net., vol. 3, 1990, pp. 535-549.

[31] H. Demuth, M. Beale, Neural Network Toolbox User's Guide, Version 3.0. The Math Works, Inc., 1997, pp. 5-35.

[32] D. J. C. MacKay, "Bayesian interpolation", Neural Comput., vol. 4, 1992, pp. 415-447.

[33] V. N. Vapnik, Statistical Learning Theory. NY: New York, John Wiley & Sons, 1998.

[34] G. L. Lilien, P. Kotler, Marketing Decision Making: A Model Building Approach. NY: New York, Harper and Row Publishers, 1983.