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Temporal series and neural networks: a comparative analysis of techniques in the Brazilian retail sales forecast

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Author Info

  • Claudio Felisoni de Angelo

    (FEA/USP)

  • Ronaldo Zwicker

    (FEA/USP)

  • Nuno Manoel Martins Dias Fouto

    (FEA/USP)

  • Marcos Roberto Luppe

    (FEA/USP)

Registered author(s):

    Abstract

    An important economic activity in any society regards the commercialization of assets. The retail consists exactly of the link established between the industry and the final consumer. To predict the sales is essential so that one can manage in a proper way the production and commercialization processes. In the retail, this aspect is even more important. To sale means to harmonize the concerns of those producing with those who buy. Therefore, this paper is intended to exam comparatively the application of two retail sales forecast methods in the Brazilian market: the temporal series and the neural networks. The selection of those two techniques as object of that comparison was aroused by the importance those two conceptions have assumed in the literature. Although the utilization of neural networks has provided the smallest sum of the squares of the residues, one may say that the results using models of the ARIMA type have shown to be practically equivalent.

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    Bibliographic Info

    Article provided by Fucape Business School in its journal Brazilian Business Review.

    Volume (Year): 8 (2011)
    Issue (Month): 2 (April)
    Pages: 01-21

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    Handle: RePEc:bbz:fcpbbr:v:8:y:2011:i:2:p:01-21

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    Postal: Fucape Business School Brazilian Business Review Av. Fernando Ferrari, 1358, Boa Vista CEP 29075-505 Vitória-ES
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    Web page: http://www.bbronline.com.br/
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    Related research

    Keywords: Temporal series; neural networks; sales forecast; retail.;

    References

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    1. Brockhoff, Klaus, 1983. "Group processes for forecasting," European Journal of Operational Research, Elsevier, vol. 13(2), pages 115-127, June.
    2. Segura, J. V. & Vercher, E., 2001. "A spreadsheet modeling approach to the Holt-Winters optimal forecasting," European Journal of Operational Research, Elsevier, vol. 131(2), pages 375-388, June.
    3. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    4. Sniezek, Janet A., 1989. "An examination of group process in judgmental forecasting," International Journal of Forecasting, Elsevier, vol. 5(2), pages 171-178.
    5. Franses, Philip Hans, 2008. "Merging models and experts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 31-33.
    6. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    7. Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
    8. Chu, Ching-Wu & Zhang, Guoqiang Peter, 2003. "A comparative study of linear and nonlinear models for aggregate retail sales forecasting," International Journal of Production Economics, Elsevier, vol. 86(3), pages 217-231, December.
    9. Ang, Soon & O'Connor, Marcus, 1991. "The effect of group interaction processes on performance in time series extrapolation," International Journal of Forecasting, Elsevier, vol. 7(2), pages 141-149, August.
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