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Annals of Computer Science and Information Systems, Volume 13

Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems

A Decision Support System for Demand Forecasting based on Classifier Ensemble

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DOI: http://dx.doi.org/10.15439/2017F224

Citation: Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 13, pages 35–41 ()

Full text

Abstract. Demand forecasting is the process of constructing forecasting models to estimate the quantities of several products that customers will purchase in the future. When the warehouse and the number of products grow, forecasting the demand becomes dramatically hard. Most of the demand forecasting models rely on a single classifier or a simple combination of these models. In order to improve demand forecasting accuracy, we investigate several different classifiers such as MLP, Bayesian Network, Linear Regression and SMO analyzing their accuracy and performance. Moreover, we also studied some classifier combination techniques by approaching from demand forecasting perspective. In this paper, we propose a methodology to combine various forecasting models using neural networks rather for supporting demand forecasting. The proposed methodology is tested against single classifiers and classifier ensemble models using a real dataset. Experiments indicate that the proposed methodology outperforms all the other single classifiers tested in this study and their simple combinations.

References

  1. M. L. Christopher, Logistics and Supply Chain Management, Pitman Publishing, 1992.
  2. J. T. Mentzer, W. DeWitt, J.S. Keebler, S. Min, N. W. Nix, C. D. Smith, Z. G. Zacharia, “Defining Supply Chain Management,” Journal of Business Logistics, vol. 22, no. 2, 2001.
  3. T. C. Jones, D. W. Riley, “Using Inventory for Competitive Advantage through Supply Chain Management,“ International Journal of Physical Distribution and Materials Management, vol. 15, no. 5, pp. 16-26, 1985.
  4. I. Islek, S. G. Oguducu, “A retail demand forecasting model based on data mining techniques,” in 24th International Symposium on Industrial Electronics (ISIE), Buzios, Brasil, 2015, pp. 55-60.
  5. D. H. Wolpert, “Stacked Generalization,” Neural Networks, vol. 5, pp. 241-259, 1992.
  6. S. Q. Wang, J. Yang, K. C. Chou, “Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition,” Journal of Theoretical Biology, vol. 242, no. 4, pp. 941-946, 2006.
  7. S. R. Ness, A. Theocharis, G. Tzanetakis, “Improving automatic music tag annotation using stacked generalization of probabilistic svm outputs,” in 17th ACM international conference on Multimedia, 2009, pp. 705-708.
  8. S. Kotsiantis, E. Koumanakos, D. Tezlepis, V. Tampakas, “Forecasting Fradulent Financial Statements Using Data Mining,” International Journal of Computational Science, vol. 3, no. 2, pp. 104-110, 2006.
  9. L. M. Liu, S. Bhattacharyya, S. L. Sclove, R. Chen, W.J. Lattyak, “Data Mining on Time Series: An Illustration Using Fast-Food Restaurant Franchise Data,” Computational Statistics & Data Analysis, vol. 37, pp. 455-476, 2001.
  10. K. Gilbert, “An ARIMA supply chain model,” Management Science, vol. 51, no. 2, pp. 305-310, 2005.
  11. P. C. Chang, Y. W. Wang, C. H. Liu, “The Development of a Weighted Evolving Fuzzy Neural Network for PCB Sales Forecasting,” Expert Systems with Applications, vol. 32, pp. 86-96, 2007.
  12. Z. L. Sun, T. M. Choi, Y. Yu, “Sales Forecasting Using Extreme Learning Machine With Applications In Fashion Retailing,” Decision Support Systems, vol. 46, pp. 411-419, 2008.
  13. Y. Yu, T. Choi, C. Hui, “An Intelligent Fast Sales Forecasting Model for Fashion Products,” Expert Systems with Applications, vol. 38, pp. 7373-7379, 2011.
  14. S. H. Ling, Genetic Algorithm and Variable Neural Networks: Theory and Application, Lambert Academy Publishing, 2010.
  15. K. F. Au, T. M. Choi, Y. Yu, “Fashion Retail Forecasting by Evolutionary Neural Networks,” International Journal of Production Economics, vol. 114, pp. 615-630, 2008.
  16. R. S. Gutierrez, A. Solis, S. Mukhopadhyay, “Lumpy Demand Forecasting Using Neural Networks,” International Journal of Production Economics, vol. 111, pp. 409-420, 2008.
  17. T. Efendigil, S. Onut, C. Kahraman, “A Decision Support System for Demand Forecasting with Artificial Neural Networks and NeuroFuzzy Models: A Comparative Analysis,” Expert Systems with Applications, vol. 36, no. 3, pp. 6697-6707, 2009.
  18. L. Aburto, R. Weber, “Improved Supply Chain Management Based On Hybrid Demand Forecasts,” Applied Soft Computing, vol. 7, no. 1, pp. 136-144, 2007.
  19. P. Doganis, A. Alexandridis, P. Patrinos, H. Sarimveis, “Time Series Sales Forecasting For Short Shelf-Life Food Products Based On Artificial Neural Networks And Evolutionary Computing,” Journal Of Food Engineering, vol. 75, pp. 196-204, 2006.
  20. A. Ghorbani, K. Owrangh, “Stacked Generalization In Neural Networks: Generalization On Statistically Neutral Problems,” in IEEE International Joint Conference on Neural Networks, 2001, pp. 1715- 1720.
  21. D. W. Fan, P. K. Chan, S. J. Stolfo, “A comparative evaluation of combiner and stacked generalization,” in AAAI-96 workshop on Integrating Multiple Learned Models, 1996, pp. 40-46.
  22. G. Sigletos, G. Paliouras, C. D. Spyropoulos, M. Hatzopoulos, “Combining information extraction systems using voting and stacked generalization,” Journal of Machine Learning Research, vol. 6, pp. 1751-1782, 2005.
  23. L. Breiman, “Stacked regressions,” Machine learning, vol. 24, no. 1, pp. 49-64, 1996.
  24. I. Guler, E. D. Ubeyli “ECG beat classifier designed by combined neural network model,” Pattern recognition, vol. 38, no. 2, pp. 199-208, 2005.
  25. K. M. Ting, I. A. Witten “Issues in Stacked Generalization,” Journal Of Artificial Intelligence Research, vol. 10, pp. 271-289, 1999.
  26. A. Majumder, A. Ekbal, S. K. Naskar, “Biomolecular Event Extraction using a Stacked Generalization based Classifier,” in 13th International Conference on Natural Language Processing, 2016.
  27. H. Gandhi, D. Green, J. Kounios, C. M. Clark, R. Polikar, “Stacked Generalization for Early Diagnosis of Alzheimer's Disease,” in 28th IEEE EMBS Annual International Conference, 2006.
  28. C. F. Tsai, “Training Support Vector Machines Based On Stacked Generalization For Image Classification,” Neurocomputing, vol. 64, pp. 479-503, 2005.
  29. M. Doumpos, C. Zopounidis, “Model Combination For Credit Risk Assessment: A Stacked Generalization Approach,” Annals of Operations Research, vol. 151, no. 1, pp. 289-306, 2007.
  30. G. Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C. Spyropoulos, P. Stamatopoulos, “Stacking Classifiers for Anti-Spam Filtering of E-mail,” in Empirical Methods in Natural Language Processing, pp. 44-50, 2001.
  31. X. Liu, L. Yu, P. Peng, F. Lu, “A Stacked Generalization Framework for City Traffic Related Geospatial Data Analysis,” in Asia-Pacific Web Conference, 2016, pp. 265-276.
  32. N. Friedman, D. Geiger, M. Goldszmidt, “Bayesian Network Classifiers,” Machine Learning, vol. 29, no. 2-3, pp. 131-163, 1997.
  33. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1998.
  34. D. A. Freedman, Statistical Models: Theory and Practice, Cambridge University Press, 2009.
  35. D. Basak, S. Pal, D. C. Patranabis, “Support vector regression,” Neural Information Processing-Letters and Reviews, vol. 11(10), pp. 203-224, 2007.